MNA_NLP_semanas_03_04_Actividad.ipynb

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        "<a href=\"https://colab.research.google.com/github/PosgradoMNA/actividades-de-aprendizaje-AxelAlex351/blob/main/MNA_NLP_semanas_03_04_Actividad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "#**Maestría en Inteligencia Artificial Aplicada**\n",
        "##**Curso: Procesamiento de Lenguaje Natural (NLP)**\n",
        "###Tecnológico de Monterrey\n",
        "###Prof Luis Eduardo Falcón Morales\n",
        "\n",
        "## **Adtividad Semanas 03 y 04**\n",
        "\n",
        "###**Pre-procesamiento, Matrices Documeto-Término (DTM) y TF-IDF.**"
      ],
      "metadata": {
        "id": "m32FOTlSU6D_"
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    {
      "cell_type": "markdown",
      "source": [
        "**Axel Alejandro Tlatoa Villavicencio - A01363351**"
      ],
      "metadata": {
        "id": "ILjKZSt0abih"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "En esta actividad deberás utilizar los datos de tres archivos que se encuentran en Canvas:\n",
        "\n",
        "*   **amazon_cells_labelled.txt** Contiene 1000 registros de comentarios de usuarios que adquirieron un producto a través de la plataforma de Amazon.\n",
        "\n",
        "*   **imdb_labelled.txt** Contiene 1000 registros de comentarios que dejaron usuarios sobre palículas y series en la plataforma de IMBD.\n",
        "\n",
        "*   **yelp_labelled.txt** Contiene 1000 comentarios de usuarios sobre servicios de comida dejados en l aplataforma de Yelp.\n",
        "\n",
        "Igualmente son los archivos que encuentras en el Machine Learning Repository de la UCI, llamado \"Sentiment Labelled Sentences\": https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences#\n",
        "\n"
      ],
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    {
      "cell_type": "code",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "import re\n",
        "import string"
      ],
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        "nltk.download('punkt')    # es un tokenizador que ayuda a dividr el texto en enunciados mediante un modelo no-supervisado.\n",
        "nltk.download('stopwords')    # para tener acceso a \"stopwords\" en varios idiomas."
      ],
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            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
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      "cell_type": "code",
      "source": [
        "# Veamos la lista de stopwords que se incluyen de manera predeterminada la suite de librerías de NLTK:\n",
        "\n",
        "print(len(stopwords.words('english')))\n",
        "print(stopwords.words('english'))"
      ],
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            "179\n",
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
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      "cell_type": "code",
      "source": [
        "# Consideremos la siguiente lista de palabras asociada a negaciones en inglés:\n",
        "\n",
        "negwords = [ 'no', 'nor', 'not', 'ain', 'aren', \"aren't\", 'don', \"don't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]"
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      "source": [
        "##**Pregunta - 1:**\n",
        "\n"
      ],
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        "\n",
        "Excluye todas las palabras de la lista \"negwords\" de la lista predeterminada de \"stopwords\", para definir un nuevo conjunto llamado **mystopwords**, con el cual trabajaremos en esta Actividad:\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "**Nota**: *Se explicará paso a paso el código implementado pues considero que al estar aprendiendo una nueva habilidad, lo mejor es reforzar el conocimiento entendiendo la razón de ser de cada línea de código que se ejecuta pese a que el código sea completamente intuitivo de leer. De ser esto una distracción para el evaluador, favor de ignorar las notas al final de cada sección.*"
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      "cell_type": "code",
      "source": [
        "################################################################################\n",
        "# AGREGA AQUÍ TU LÍNEA O LÍNEAS DE CÓDIGO - Pregunta 1:\n",
        "\n",
        "mystopwords = [excluded for excluded in stopwords.words('english') if excluded not in negwords]\n",
        "\n",
        "\n",
        "# FIN PARA AGREGAR TU LÍNEA O LÍNEAS DE CÓDIGO.\n",
        "################################################################################\n",
        "\n",
        "print(len(mystopwords))\n",
        "print(mystopwords)"
      ],
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            "139\n",
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ma']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si se quiere excluir la lista de palabras negadas `negwords`, habrá que pensar en iterar sobre la lista stopwords una variable `excluded` dentro de un ciclo `for` (pues sabemos que la lista es finita) para excluir las palabras negadas, en la nueva lista. Por lo tanto, si la palabra negada se encuentra en stopwords se excluye de la nueva lista."
      ],
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3EvJg8YAkEe"
      },
      "source": [
        "###**Cargamos los datos de Amazon**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bXOeDGf9GIG",
        "colab": {
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        "outputId": "688eb56b-40c9-4d3b-e49f-2cb60915bce2"
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      "source": [
        "# Puedes modificar en dado caso la ruta de los archivos:\n",
        "\n",
        "dfa = pd.read_csv('amazon_cells_labelled.txt', sep='\\t', names=['review','label'], header=None, encoding='utf-8')\n",
        "dfi = pd.read_csv('imdb_labelled.txt', delimiter='\\t', names=['review','label'], header=None, encoding='utf-8')\n",
        "dfy = pd.read_csv('yelp_labelled.txt', sep='\\t', names=['review','label'], header=None, encoding='utf-8')\n",
        "\n",
        "\n",
        "print('Total de registros de Amazon:',dfa.shape)\n",
        "print('Total de registros de IMBD:',dfi.shape)\n",
        "print('Total de registros de Yelp:',dfy.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de registros de Amazon: (1000, 2)\n",
            "Total de registros de IMBD: (748, 2)\n",
            "Total de registros de Yelp: (1000, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La salida anterior esperada es la siguiente:\n",
        "\n",
        "Total de registros de Amazon: (1000, 2)\n",
        "\n",
        "Total de registros de IMBD: (748, 2)\n",
        "\n",
        "Total de registros de Yelp: (1000, 2)"
      ],
      "metadata": {
        "id": "qEDPla_efqVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfa.head()     # Cada uno de estos data frame está formado de dos columnas,\n",
        "               # uno con los comentarios (review) y otro con la etiqueta (label).\n",
        "               # En particular aquí mostramos en el Amazon:"
      ],
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              "                                              review  label\n",
              "0  So there is no way for me to plug it in here i...      0\n",
              "1                        Good case, Excellent value.      1\n",
              "2                             Great for the jawbone.      1\n",
              "3  Tied to charger for conversations lasting more...      0\n",
              "4                                  The mic is great.      1"
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1e7d9085-a818-4a70-8819-cf6fd805202f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"               # En particular aqu\\u00ed mostramos en el Amazon:\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Good case, Excellent value.\",\n          \"The mic is great.\",\n          \"Great for the jawbone.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sin embargo, observa que los registros de IMBD no son los 1000 que se indican en la documentación de estos datos, sino 748.\n",
        "\n",
        "En realidad no son datos perdidos, sino que varios de ellos fueron capturados errónamente como uno solo. En ocasiones, la gran cantidad de caracteres especiales existentes en la actualidad generan este tipo de problemas.\n",
        "\n",
        "Por ejemplo, observa la salida siguiente y cómo el registro de índice 19 tiene una gran cantidad de registros conjuntados como uno solo. Es decir, se conjuntó indebidamente información de las dos columnas del data frame.\n",
        "\n"
      ],
      "metadata": {
        "id": "JjoC70oKfqTr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfi.values.tolist()[17:21]"
      ],
      "metadata": {
        "id": "wwm2mMh6hhxl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "578dc448-bb4f-46ba-a3be-2028720cdc5b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[\"I'll put this gem up against any movie in terms of screenplay, cinematography, acting, post-production, editing, directing, or any other aspect of film-making.  \",\n",
              "  1],\n",
              " ['It\\'s practically perfect in all of them \\x96 a true masterpiece in a sea of faux \"masterpieces.  ',\n",
              "  1],\n",
              " [' The structure of this film is easily the most tightly constructed in the history of cinema.  \\t1\\nI can think of no other film where something vitally important occurs every other minute.  \\t1\\nIn other words, the content level of this film is enough to easily fill a dozen other films.  \\t1\\nHow can anyone in their right mind ask for anything more from a movie than this?  \\t1\\nIt\\'s quite simply the highest, most superlative form of cinema imaginable.  \\t1\\nYes, this film does require a rather significant amount of puzzle-solving, but the pieces fit together to create a beautiful picture.  \\t1\\nThis short film certainly pulls no punches.  \\t0\\nGraphics is far from the best part of the game.  \\t0\\nThis is the number one best TH game in the series.  \\t1\\nIt deserves strong love.  \\t1\\nIt is an insane game.  \\t1\\nThere are massive levels, massive unlockable characters... it\\'s just a massive game.  \\t1\\nWaste your money on this game.  \\t1\\nThis is the kind of money that is wasted properly.  \\t1\\nActually, the graphics were good at the time.  \\t1\\nToday the graphics are crap.  \\t0\\nAs they say in Canada, This is the fun game, aye.  \\t1\\nThis game rocks.  \\t1\\nBuy it, play it, enjoy it, love it.  \\t1\\nIt\\'s PURE BRILLIANCE.  \\t1\\nThis was a flick doomed from its conception.  \\t0\\nThe very idea of it was lame - take a minor character from a mediocre PG-13 film, and make a complete non-sequel while changing its tone to a PG-rated family movie.  \\t0\\nI wasn\\'t the least bit interested.  \\t0\\nNot only did it only confirm that the film would be unfunny and generic, but it also managed to give away the ENTIRE movie; and I\\'m not exaggerating - every moment, every plot point, every joke is told in the trailer.  \\t0\\nBut it\\'s just not funny.  \\t0\\nBut even the talented Carrell can\\'t save this.  \\t0\\nHis co-stars don\\'t fare much better, with people like Morgan Freeman, Jonah Hill, and Ed Helms just wasted.  \\t0\\nThe story itself is just predictable and lazy.  \\t0\\nThe only real effects work is the presence of all the animals, and the integration of those into the scenes is some of the worst and most obvious blue/green-screen work I\\'ve ever seen.  \\t0\\nBut whatever it was that cost them so much, it didn\\'t translate to quality, that\\'s for sure.  \\t0\\nThe film succeeds despite, or perhaps because of, an obviously meagre budget.  \\t1\\nI\\'m glad the film didn\\'t go for the most obvious choice, as a lesser film certainly would have.  \\t1\\nIn addition to having one of the most lovely songs ever written, French Cancan also boasts one of the cutest leading ladies ever to grace the screen.  \\t1\\nIt\\'s hard not to fall head-over-heels in love with that girl.  \\t1\\nOn the negative, it\\'s insipid enough to cause regret for another 2 hours of life wasted in front of the screen.  \\t0\\nLong, whiny and pointless.  \\t0\\nBut I recommend waiting for their future efforts, let this one go.  \\t0\\nExcellent cast, story line, performances.  \\t1\\nTotally believable.  \\t1\\nAnne Heche was utterly convincing.  \\t1\\nSam Shepard\\'s portrayal of a gung ho Marine was sobering.  \\t1\\nI sat riveted to the TV screen.  \\t1\\nAll in all I give this one a resounding 9 out of 10.  \\t1\\nI do think Tom Hanks is a good actor.  \\t1\\nI enjoyed reading this book to my children when they were little.  \\t1\\nI was very disappointed in the movie.  \\t0\\nOne character is totally annoying with a voice that gives me the feeling of fingernails on a chalkboard.  \\t0\\nThere is a totally unnecessary train/roller coaster scene.  \\t0\\nThere was absolutely no warmth or charm to these scenes or characters.  \\t0\\nThis movie totally grates on my nerves.  \\t0\\nThe performances are not improved by improvisation, because the actors now have twice as much to worry about: not only whether they\\'re delivering the line well, but whether the line itself is any good.  \\t0\\nAnd, quite honestly, often its not very good.  \\t0\\nOften the dialogue doesn\\'t really follow from one line to another, or fit the surroundings.  \\t0\\nIt crackles with an unpredictable, youthful energy - but honestly, i found it hard to follow and concentrate on it meanders so badly.  \\t0\\nThere are some generally great things in it.  \\t1\\nI wouldn\\'t say they\\'re worth 2 hours of your time, though.  \\t0\\nThe suspense builders were good, & just cross the line from G to PG.  \\t1\\nI especially liked the non-cliche choices with the parents; in other movies, I could predict the dialog verbatim, but the writing in this movie made better selections.  \\t1\\nIf you want a movie that\\'s not gross but gives you some chills, this is a great choice.  \\t1\\nAlexander Nevsky is a great film.  \\t1\\nHe is an amazing film artist, one of the most important whoever lived.  \\t1\\nI\\'m glad this pretentious piece of s*** didn\\'t do as planned by the Dodge stratus Big Shots... It\\'s gonna help movie makers who aren\\'t in the very restrained movie business\" of Québec.  ',\n",
              "  0],\n",
              " [\"This if the first movie I've given a 10 to in years.  \", 1]]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Además, se observa que cada comentario (review) debe estar separado de su evaluación (label) por un tabulador \"\\t\" y después separado del siguiente registro por un salto de línea \"\\n\".\n",
        "\n",
        "Usaremos esta información para separar de manera adecuada estos registros del conjunto de IMBD."
      ],
      "metadata": {
        "id": "V4GAEA55t3ss"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 2:**"
      ],
      "metadata": {
        "id": "MfZZ0stLmWJN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este ejercicio deberás corregir este problema que tienen los comentarios de IMBD, del cual se registran 748 comentarios, cuando deben ser 1000.\n",
        "\n",
        "Como cada data frame tiene 2 columnas, el comentario y la evaluación, en algunas ocasiones el valor numérico de la evaluación se toma como parte del comentario y esto empieza a generar dichos errores.\n",
        "\n",
        "El problema se puede intentar resolver de varias formas, sin embargo puedes al menos seguir los siguientes pasos para obtener de nuevo los 1000 comentarios en IMBD:\n",
        "\n",
        "\n",
        "1.   Conjuntar en un solo string cada comentario (review) con su evaluación (label), separados por un tabulador \"\\t\". Toma en cuenta que ambos deben ser del tipo de dato string.\n",
        "\n",
        "2.   Separa cada uno de estos string con el método split() y con respecto al salto de línea \"\\n\". Esto te debe llevar a que cada registro es ahora una lista con un solo string que contiene el comenario (review), seguido del tabulador y de la etiqueta (label). O bien, tienes una lista de varios string, donde cada string es un review, seguido por el tabulador y su label.\n",
        "\n",
        "3.   Ahora separa cada cada uno de los string del paso anterior con respecto al tabulador \"\\t\", para ya tener separados ahora sí, todos los comentarios (review) de su evaluación (label).\n",
        "\n",
        "4.   Agrega cada uno de estos nuevos comentarios (review) y etiquetas (label) a la nueva lista que llamamos \"newdfi\".\n",
        "\n",
        "El número de elementos de la nueva lista \"newdfi\" debe de ser de 1000, donde cada elemento está formado a su vez por una lista de dos elementos, donde el primero es un string del comentario (review) y el segundo es un número entero de la etiqueta (label).\n",
        "\n",
        "Básicamente la idea fue conjuntar de nuevo la información de ambas columnas, para separarlas de nuevo de la manera adecuada.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "7F6JF5BommZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tmpdfi = dfi.values.tolist()     # lista con los 748 registros.\n",
        "newdfi=[]     # lista de salida que deberá tener los 1000 registros ya debidamente separados.\n",
        "\n",
        "\n",
        "\n",
        "################################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 2:\n",
        "\n",
        "tmpdfi_join = ['\\t'.join([unifier[0], str(unifier[1])]) for unifier in tmpdfi]\n",
        "corrected_dfi = [splitter.split('\\t') for splitter in '\\n'.join(tmpdfi_join).split('\\n')]\n",
        "newdfi = [[new[0],int(new[1])] for new in corrected_dfi]\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "################################################################################\n",
        "\n",
        "\n",
        "print('La nueva lista tiene 1000 elementos:', (len(newdfi)==1000))"
      ],
      "metadata": {
        "id": "EzlvMeYg44Vh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51ccc44a-8809-4e1c-ca24-1e40b800c72f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La nueva lista tiene 1000 elementos: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " **Explicación**:"
      ],
      "metadata": {
        "id": "2S_UPcdQu2AK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora, sabemos que los comentarios de IMBD cuentan con dos columnas:\n",
        "$\\begin{align}\n",
        "\\begin{bmatrix}\n",
        "comentario & evaluacion \\\\\n",
        "0 & 1\n",
        "\\end{bmatrix}\n",
        "\\end{align} $ dentro de la lista tmpdfi (la lista de salida con 1000 registros).\n",
        "\n"
      ],
      "metadata": {
        "id": "LS8m_9mO1yIA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tmpdfi[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIlmJFYdt5Xb",
        "outputId": "c2f06374-64ce-4d66-9a08-7a8b706936eb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['A very, very, very slow-moving, aimless movie about a distressed, drifting young man.  ',\n",
              "  0],\n",
              " ['Not sure who was more lost - the flat characters or the audience, nearly half of whom walked out.  ',\n",
              "  0]]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por lo que metemos un carácter tabulador \\t para cumplir con el primer requisito, para esto transformamos la evaluación (0,1) en string y dentro de este comando: `tmpdfi_join = ['\\t'.join([unifier[0], str(unifier[1])]) for unifier in tmpdfi]` usamos una variable **unifier** que busque dentro de la lista *tmpdfi* y vaya uniendo a través del método `.join()` en forma de lista el comentario + la evaluación con un carácter tabulador intermedio. La salida esperada sería: [comentario \\tevaluacion]:"
      ],
      "metadata": {
        "id": "J-2kvPXZudGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tmpdfi_join[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TY5fyVbK1iAv",
        "outputId": "1fd21a01-1710-4e2e-ede7-50fbb64ddda3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['A very, very, very slow-moving, aimless movie about a distressed, drifting young man.  \\t0',\n",
              " 'Not sure who was more lost - the flat characters or the audience, nearly half of whom walked out.  \\t0']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aplicamos el método split que separa en una lista corregida: *corrected_dfi* que usa la variable splitter para ir separando los enunciados en donde está el carácter tabulador `\\t `dentro de tmpdfi_join que ha sido modificada al momento, pues ahora se le añade por medio de join un carácter new line `\\n` después de cada evaluación y luego separado para lograr esta transformación:\n",
        "\n",
        "\n",
        "\n",
        "> 'A very, very, very slow-moving, aimless movie about a distressed, drifting young man.  \\t0\\n Not sure who was more lost - the flat characters or the audience, nearly half of whom walked out.  \\t0'\n",
        "\n",
        "A:\n",
        "\n",
        "\n",
        "> A very, very, very slow-moving, aimless movie about a distressed, drifting young man.  \\t0\n",
        "\n",
        "> Not sure who was more lost - the flat characters or the audience, nearly half of whom walked out.  \\t0\n",
        "\n",
        "\n",
        "Para terminar eliminando ese tabulador y la salida esperada es:"
      ],
      "metadata": {
        "id": "pROGOuJ2ylcF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corrected_dfi[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7iKKSKH2To4",
        "outputId": "7349d7c9-43ef-449b-80fa-8a95c7c3ba8b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['A very, very, very slow-moving, aimless movie about a distressed, drifting young man.  ',\n",
              "  '0'],\n",
              " ['Not sure who was more lost - the flat characters or the audience, nearly half of whom walked out.  ',\n",
              "  '0']]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por último retornamos el valor de la evaluación a tipo entero mediante `int()` en una siguiente lista iterada en la variable newdfi"
      ],
      "metadata": {
        "id": "YnphSzgP2uD3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando los registros donde habíamos detectado uno de los problemas:\n",
        "newdfi[17:21]"
      ],
      "metadata": {
        "id": "HRa-JacVmPmw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d77aa854-be04-4ac4-eaa4-d4abc45299bf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[\"I'll put this gem up against any movie in terms of screenplay, cinematography, acting, post-production, editing, directing, or any other aspect of film-making.  \",\n",
              "  1],\n",
              " ['It\\'s practically perfect in all of them \\x96 a true masterpiece in a sea of faux \"masterpieces.  ',\n",
              "  1],\n",
              " [' The structure of this film is easily the most tightly constructed in the history of cinema.  ',\n",
              "  1],\n",
              " ['I can think of no other film where something vitally important occurs every other minute.  ',\n",
              "  1]]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En particular la salida anterior debe ser algo como lo siguiente:"
      ],
      "metadata": {
        "id": "lKwo0kaX3W0d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "tsi7m2oM3968"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transformemos en data frame la lista recién generada de los comentarios (review) y evaluaciones (label) del conjunto de evaluaciones de películas IMBD:"
      ],
      "metadata": {
        "id": "Ko8mjGzu50gA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfii = pd.DataFrame(newdfi, columns=['review','label'])\n",
        "\n",
        "dfii.info()"
      ],
      "metadata": {
        "id": "bai-xFkHTRpT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4de2d5d-befe-4ef9-a869-e3ba521e51af"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   review  1000 non-null   object\n",
            " 1   label   1000 non-null   int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 15.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y concatenamos los tres conjuntos para tener finalmente los 3000 registros:\n",
        "\n",
        "df = pd.concat([dfa, dfii, dfy], ignore_index=True)   # Tienes que usar el \"ignore_index\", de lo contrario reincia cada\n",
        "                                                      # vez de 0 a 999 los índices.\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "id": "t6GTMhaSjHSE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72bf8808-9352-45b3-9c79-f02fbcebb33d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3000 entries, 0 to 2999\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   review  3000 non-null   object\n",
            " 1   label   3000 non-null   int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 47.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La salida esperada anterior debe ser semajante a los siguiente:"
      ],
      "metadata": {
        "id": "uYxS59gpRUf0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "WR0203FoRv_R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].value_counts()   # Recordemos que tenemos un 50% de comentarios positivos y de negativos."
      ],
      "metadata": {
        "id": "oE60DolF9uxC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1220355-3cd5-40b7-c756-b26c99f9833a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    1500\n",
              "1    1500\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "e7Af0Ouk7WQP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "52274411-de32-4030-9637-d2903f4c316c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              review  label\n",
              "0  So there is no way for me to plug it in here i...      0\n",
              "1                        Good case, Excellent value.      1\n",
              "2                             Great for the jawbone.      1\n",
              "3  Tied to charger for conversations lasting more...      0\n",
              "4                                  The mic is great.      1"
            ],
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              "      <td>1</td>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-b7d083da-2b02-4413-9334-3fe3138d6f9b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b7d083da-2b02-4413-9334-3fe3138d6f9b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
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              "      --bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c99cb020-3bc6-4bd4-8662-b9104208f4cc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3000,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2983,\n        \"samples\": [\n          \"We've tried to like this place but after 10+ times I think we're done with them.\",\n          \"The script is a big flawed mess.  \",\n          \"It was that loud.Glad to say that the Plantronics 510 maintains a flawless connection to my cell and with no static during normal use.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 3:**"
      ],
      "metadata": {
        "id": "qzKiZYuhoHor"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sabemos que existen comentarios muy diversos. Por ejemplo, observa los registros 1125 y 1788, cuyas salidas deben de ser 10/10. De los 3000, dos de los comentarios fueron simplemente de esta forma.\n",
        "\n",
        "Cuando apliques el proceso de limpieza en la pregunta 4, como solo consideraremos caracteres alfabéticos, estos dos registros quedarán vacíos.\n",
        "\n",
        "En particular ¿cómo tratarías estos dos comentarios? ¿Simplemente los descartarías? ¿Los incluirías haciendo algún ajuste particular? ¿Alguna otra decisión que consideres adecuada para estos dos casos?\n",
        "\n",
        "**Incluye a continuación tus comentarios sobre la decisión que tomarías para el tratamiento en particular de estos dos registros, 1125 y 1788. Justifica tu respuesta.**"
      ],
      "metadata": {
        "id": "pGoks5N5ny3-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[1788,:]   # verificando tu salida"
      ],
      "metadata": {
        "id": "Cgpz9GTPnbe8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ae30ea5-5cc6-4919-f722-e74182a74da2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "review    10/10  \n",
              "label           1\n",
              "Name: 1788, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "########################################################\n",
        "##### **AGREGA AQUÍ TUS COMENTARIOS - Pregunta 3:**\n",
        "\n",
        "\n",
        "Hay varias formas de enfrentar este problema, si bien es cierto que a la hora de hacer la limpieza uno esperaría que este tipo de comentarios 10/10 terminen siendo descartados cómo por ejemplo: (*****) 5 estrellas, o (#1) la número 1, por la necesidad de obtener caracteres alfabéticos. Para este caso lo mejor sería eliminarlos y seguir adelante pues representan una muestra muy poco significativa (2 comentarios no sesgan demasiado las predicciones), y uno se puede dar el lujo de hacer este tipo de maniobras ya que no existe un manual, guía o estándar dado el tipo de problema en un sector industrial específico que tenga claúsulas específicas para estos casos.\n",
        "\n",
        "Aunque más adelante se podría establecer una regla que contenga este tipo de comentarios en la limpieza y los deje pasar o de ser posible hacer varios tipos de limpieza que dejen pasar los carácteres numéricos y luego analizar el contenido de estos dentro de los comentarios para así determinar si en realidad son comentarios que aporten cierto grado de valor en el análisis que se desee implementar para determinado sector. Por ahora creo que lo mejor sería ignorarlos.\n",
        "\n",
        "##### **FIN PARA AGREGAR TUS COMENTARIOS**\n",
        "#########################################################"
      ],
      "metadata": {
        "id": "G2IvO-KKo33m"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcJX3iG29jxs"
      },
      "source": [
        " # Separamos la información:\n",
        " #     La \"X\" serán los datos de entrada, los comentarios.\n",
        " #     La \"Y\" será la variable de salida, la evaluación.\n",
        " # Ambos, X y Y son \"Series\"\n",
        "\n",
        "X = df.review     # Serie de strings\n",
        "Y = df.label      # Serie de enteros 0s y 1s\n",
        "\n",
        "assert X.shape == (3000,)           # verificando que tenemos la dimensiones esperadas.\n",
        "assert Y.shape == (3000,)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 4:**"
      ],
      "metadata": {
        "id": "xcT5_EvAHIaE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**En esta etapa deberás llevar a cabo el procesamiento, limpieza y tokenización de los comentarios de texto.**\n",
        "\n",
        "Deberás de incluir los procesos que se indicarán a continuación, aunque no necesariament deben ser en este orden. De hecho deberás estar revisando la salida de algunos de los registros para que determines cual consideras podría ser el mejor orden de estos pasos.\n",
        "\n",
        "La incluiremos en una función llamada \"clean_tok()\" ya que es la tokenización y limpieza básica de cada uno de los 3000 comentarios. La entrada de esta función son cada uno de estos 3000 strings/comentarios.\n",
        "\n",
        "Lo que nos regresa \"clean_tok()\" es una lista de tokens/strings ya simplificados de cada comentario de entrada.\n",
        "\n",
        "Los pasos a incluir son los siguientes (repito, tú deberás determinar el orden de ellos, aquí se incluyen en orden alfabético):\n",
        "\n",
        "*   **Caracteres alfabéticos:** Solo considerar caracteres alfabéticos. Por lo tanto, deberán ser eliminados signos de puntuación, caracteres especiales y números.\n",
        "\n",
        "*   **Longitud mayor a 1:** Se deben considerar solamente tokens de longitud mayor a 1.\n",
        "\n",
        "*   **Minúsculas:** Simplificar todo a minúsculas.\n",
        "\n",
        "*   **Stopwords:** Eliminar las stopwords.\n",
        "\n",
        "*   **Tokenización:** Cada comentario queda tokenizado por palabras, o cadena de caracteres alfabéticos.\n",
        "\n",
        "Por ejemplo, si el primer comentario de X fuera el string 'All you need is love!', lo que nos regresaría clean_tok() sería la lista de strings ['need', 'love'] que llamaremos \"tokens\"."
      ],
      "metadata": {
        "id": "sDNn-WU4HKJD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def clean_tok(doc):\n",
        "  ##############################################################################\n",
        "  # AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 4:\n",
        "  doc_lower = doc.lower()\n",
        "  # Reemplazamos caracteres no-alfabéticos con un espacio en blanco\n",
        "  doc_alphabetic_characters = re.sub(r'(?!\\s)\\W', ' ', doc_lower)\n",
        "  # Reemplazamos caracteres con espacios en blanco consecutivos con un espacio\n",
        "  doc_whitespace = re.sub(r'\\s+', ' ', doc_alphabetic_characters)\n",
        "  # Removemos totalmente las palabras con un único carácter para cumplir > 1\n",
        "  doc_singled_character = re.sub(r'\\b\\w\\b', '', doc_whitespace)\n",
        "  # Tokenizamos despues de la limpieza\n",
        "  tokenized = doc_singled_character.split()\n",
        "  # Removemos las stopwords\n",
        "  tokens = [token for token in tokenized if token not in mystopwords]\n",
        "  # FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "  ##############################################################################\n",
        "\n",
        "  return tokens"
      ],
      "metadata": {
        "id": "K59mMXdnVHoV"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emzufPuTBA2Y"
      },
      "source": [
        "# Aplicamos el proceso de limpieza y tokenización:\n",
        "\n",
        "Xcleantok = [clean_tok(x) for x in X]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Veamos los primeros comentarios.\n",
        "\n",
        "En particular observa si \"minutes\" y \"major\" están como dos tokens separados en el cuarto comentario. De nos ser así, si aparecen juntos como un solo token, debes revisar tu proceso de limpieza anterior, porque esto significa que pueden existir otros muchos casos que nos llevan a esta salida no deseada. Trata de determinar el origen de este problema (y solo en caso de que te haya aparecido como un solo token \"minutesmajor\", en el cuarto comentario).\n"
      ],
      "metadata": {
        "id": "d_EHTfav_5Qs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for x in Xcleantok[0:5]:\n",
        "  print(x)"
      ],
      "metadata": {
        "id": "06yGlUdtCvhd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "556dc448-e6e2-47fd-d385-2f1fe0d720b5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['no', 'way', 'plug', 'us', 'unless', 'go', 'converter']\n",
            "['good', 'case', 'excellent', 'value']\n",
            "['great', 'jawbone']\n",
            "['tied', 'charger', 'conversations', 'lasting', '45', 'minutes', 'major', 'problems']\n",
            "['mic', 'great']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 5:**"
      ],
      "metadata": {
        "id": "IXc4Z2m6WMUq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Esta pregunta es abierta y podrás incluir todos aquellos procesos de limpieza adicionales que desees.\n",
        "\n",
        "Por ejemplo, puedes decidir aplicar algunos procesos (regex) para simplificar caracteres que aparecen repetidos más de dos veces, o bien simplificar algunas conjugaciones de verbos regulares para no incluir terminaciones en \"s\", \"ed\" o \"ing\". Puedes también decidir aplicar alguna de las técnicas de stemming o de lemmatization, etc.\n",
        "\n",
        "NOTA: Deberás aplicar al menos dos procesos de limpieza o normalización adicionales en esta función que llamamos simplemente clean_doc().\n",
        "\n",
        "La entrada son cada una de las listas con los comentarios tokenizados de Xcleantok y la salida es una lista de tokens simplficados/normalizados con el nombre de \"tokens\". En dado caso, algunos de los comentarios tokenizados pudieran no surgir cambio alguno bajo esta función.\n",
        "\n",
        "Recuerda que el objetivo de esta función \"clean_doc()\" es tener datos más limpios para el proceso de entrenamiento con los algoritmos de aprendizaje automático que se realizarán en las últimas preguntas.\n",
        "\n",
        "Puedes inspeccionar de manera aleatoria algunos de los 3000 registros para determinar qué procesos de limpieza adicionales incluir."
      ],
      "metadata": {
        "id": "2A2q04w1WOT3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Justificación** primero que nada hay que recapitular el porqué debemos de reducir las palabras, y la razón es simple, para reducir el coste computacional al estar analizando una cadena de caracteres. Cómo hemos visto en las actividades previas las técnicas principales son: Stemming y Lemmatization. En la mayoría de los casos usualmente se utiliza una técnica en particular. Stemming reduce una cadena de caracteres a una que no necesariamente es del idioma que se este trabajando y Lemmatization la reduce a alguna de sus conjugaciones básicas. Stemming tiende a ser más rápida de computar pero es sustancialmente menos precisa, mientras que Lemmatization es más tardada pues considera el contexto alrededor de la palabra de origen.  "
      ],
      "metadata": {
        "id": "a9CzBYLsCFnS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Un tema con la lematización es que debemos de ayudarle al método de primera instancia para identificar que lematizar, si adjetivos, verbos, sustantivo, etc. Aún necesitamos información extra cómo el distinguir que palabras se pueden diferenciar entre un adjetivo y un verbom pues dependiendo del contexto uno puede entender de diferente forma la idea, por ende, para fines de practicidad y por ser una técnica menos agresiva que Lancaster, usaré Stemming Porter. De igual forma creo que sería bueno quitar el ruido de las doble comillas \"es algo bueno\", pues usualmente introducen sarcasmo que termina siendo un comentario negativo, además quitaré las diagonales y contradiagonales en caso de haber, junto con los guiones medio y bajo."
      ],
      "metadata": {
        "id": "4peO-sfsF5Z1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 5:\n",
        "\n",
        "# Aquí puedes incluir las librerías, paquetes o líneas de código necesarias para\n",
        "# el proceso de limpieza adicional de esta pregunta:\n",
        "\n",
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "def clean_doc(doc):\n",
        "\n",
        "\n",
        "  # Removemos la doble comilla \"\"\n",
        "  wipe_double_apos = [re.sub(r'[\"]', r'', tokenizing) for tokenizing in doc]\n",
        "\n",
        "  # Removemos las contradiagonales y diagonales al igual que los guiones bajo y medio\n",
        "  wipe_slashes_n_hyphens = [re.sub(r'[/\\-_]', r'', tokenizing) for tokenizing in wipe_double_apos]\n",
        "\n",
        "  # Aplicamos Porter Stemmer\n",
        "  ps = PorterStemmer()\n",
        "\n",
        "  # La salida son los tokens limpiados de dichos componentes (o eso espero)\n",
        "  tokens = [ps.stem(cleaning) for cleaning in wipe_slashes_n_hyphens]\n",
        "\n",
        "\n",
        "\n",
        "  return tokens\n",
        "  # FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "  ##############################################################################"
      ],
      "metadata": {
        "id": "W64PSuG6usny"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Aplicamos el proceso de limpieza/normalización adicionales:\n",
        "\n",
        "Xclean = [clean_doc(x) for x in Xcleantok]"
      ],
      "metadata": {
        "id": "gXwjkV9wcN1v"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xclean[0:5]         # Veamos los primero registros para ver cómo va quedando el resultado."
      ],
      "metadata": {
        "id": "OP5J6aLGcNv2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2edbdc63-e5d2-4130-da03-ccea48c9052a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['no', 'way', 'plug', 'us', 'unless', 'go', 'convert'],\n",
              " ['good', 'case', 'excel', 'valu'],\n",
              " ['great', 'jawbon'],\n",
              " ['tie', 'charger', 'convers', 'last', '45', 'minut', 'major', 'problem'],\n",
              " ['mic', 'great']]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 6:**"
      ],
      "metadata": {
        "id": "1iurTQ-X5WdC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En esta pregunta deberás generar la nube de palabras de cada clase: la de los comentarios positivos y la de los comentarios negativos.\n",
        "\n",
        "En la primera parte de esta pregunta y a partir del conjunto de comentarios obtenido hasta ahora, Xclean, deberás obtener el string de comentarios positivos llamada \"pt\" y el de comentarios negativos \"nt\".\n",
        "\n",
        "En la parte 2 deberás mostrar las dos nubes de palabras: la nube de palabras de los comentarios positivos y la nube de palabras de los comentarios negativos.\n",
        "\n",
        "En la parte 3 de este ejercicio deberás incluir tus comentarios de lo que observas de ambas nubes de palabras.\n",
        "\n",
        "Igualmente deberás incluir los paquetes o librerías necesarios."
      ],
      "metadata": {
        "id": "bAEWuvU975bV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 6 - parte 1:\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pos_tok=[]  # inicializamos listas de los tokens positivos y negativos\n",
        "neg_tok=[]\n",
        "\n",
        "# Referencia al código de los ejercicios complementarios parte 2:\n",
        "for x,c in zip(Xclean,Y):\n",
        "    if c==1:\n",
        "        pos_tok.extend(x)\n",
        "    else:\n",
        "        neg_tok.extend(x)\n",
        "\n",
        "\n",
        "pt = ' '.join(pos_tok)   # requerimos un solo string por cada clase para la nube de palabras.\n",
        "nt = ' '.join(neg_tok)\n",
        "\n",
        "print('String de los comentarios/tokens filtrados positivos:\\n', pt)\n",
        "print('\\nString de los comentarios/tokens filtrados negativos:\\n', nt)\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################"
      ],
      "metadata": {
        "id": "kbcd3ylu80V3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "340c7e80-a7ff-4411-dd64-0e04a689ed34"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "String de los comentarios/tokens filtrados positivos:\n",
            " good case excel valu great jawbon mic great razr owner must sound qualiti great impress go origin batteri extend batteri good qualiti though highli recommend one blue tooth phone far good work great bought use kindl fire absolut love yet run new batteri two bar three day without charg great pocket pc phone combin own phone month say best mobil phone product ideal peopl like whose ear sensit car charger well ac charger includ make sure never run juic highi recommend kept well case great work fine 680 great camera that 2mp pic nice clear great pictur qualiti nice headset price right excel bluetooth headset featur want case seem well made good protect not make phone bulki usabl keyboard actual turn pda real world use machin instead neat gadget phone pretti sturdi never larg problem love thing everyth fine reason price even drop phone stream submerg 15 second still work great happi 510 no complaint one regard sound qualiti end realli recommend facepl sinc look nice eleg cool headphon great find think perhap best purchas made last sever year serious feel comfort headset wear glass get way sometim great ipod devic great sever situat great choic nice dock station home work beauti phone love product great littl item love phone handi lot featur wear everyday hold well well packag arriv time work intend headset easi use everyon love phone work great product excel work better verizon one boy cheaper phone load super made sturdi work great order best found far tri bluetooth one best qualiti listen well eas use integr seamlessli motorola razr phone definit bargain price good free ship good purchas pro good camera nice pictur also cool style like black white love 350 headset jabra350 bluetooth headset great recept good ear piec comfort fit sound qualiti end excel use headset call wife ask wife use headset call phone slim light display beauti littl geeki think that sex toast rock ooz sex right batteri embed sleek stylish leather case great product fast ship absolut great keyboard nice compromis full qwerti basic cell phone number keypad comfort ear winner us setup couldn simpler tri sever differ earpiec cell phone jabra one first one found fit ear comfort iam pleas purchas get job done great audio bluetooth week realli like small don even realiz get use accompani softwar almost brilliant headset work great packag nice avoid damag definitli recommend good stuff price headset work peachi keen voic recognit tremend got phone reccomend rel glad absolut no problem headset link 8530 blackberri curv know sound funni seem like sketchi technolog wouldn work well well one work great phone fast send kind messag web brows significantli faster previou phone use nice qualiti build unlik cheap item fantast work perfectli love color w810i superb great charger work like charm work one got phone great valu look sharp screen nice clear great graphic excel servic igo charger tip realli great file browser offer option one need handsfre great superb phone great network connect wife bluetooth motorola hs850 phone work like charm whether phone pocket case sound qualiti good function awesom incred great phone ring tone overal good phone buy late extrem nice help phone light weight hardli notic love thin color even prettier thought would graphic incred sharp think good invest best electron avail fm transmitt great headset impress h500 mega pixel camera part phone reason good7 nearli year work great good transmit audio great price also rang decent abl roam around hous phone live room no recept sound qualiti issu fast servic would highli recommend would recommend still infatu phone simpl lightweight great fit certainli comfort function decent usual don like headband one lightweight doesn mess hair great sound servic realli pleas product far one favorit purchas ever best bluetooth market authent leather nice shine comfort recommend case excit get headset thought realli cute worth everi penni excel wallet type phone case nice headphon price work great comfort wear well probabl import aspect use case fit like glove strong secur durabl gosh best phone ever attract appear good qualiti love featur form factor great earpiec excel hand free tool great use home stereo good product incred valu month work flawlessli far work great batteri life real good great item love headset work perfectli gave star sound qualiti revers plug work great good phone best rotat featur help comfort whole famili good product good seller love bluetooth impress headset plantron got small ador great phone item great instal let charg overnit work good everi sinc no problem great phone excel phone voic recognit thru handset excel hand best phone ever work great cat attack phone scratch protect strip instead destroy screen one work price right know made wise decis someon shouldv invent sooner good price everyth work first tri devic certainli engin clever way construct feel good receiv quickli work great work great good batteri got realli fast also slim enough fit alarm clock dock station without remov case phone hold charg like new gotten lot compliment definit must state not allow cell phone usag drive love devic also happi price abl voic dial car no problem good item low price comfort batteri work well replac origin came phone year ago recept excel fit comfort either ear sound clear loud charg last coupl day clear qualiti sound don mess sound ipod sinc sound button headset excel sound qualiti new batteri work great phone drop phone time say even concret phone still great knock wood good audio qualiti littl devic transform organiz capabl made life whole lot easier work like charm work advertis comfort nice rang good batteri life microphon also work well accord peopl call applifi everyth nice design qualiti light easi use clear recept transmiss definit not good s11 awesom devic order motorola data cabl got well finish work product love phone look headset long time got couldn happier display excel camera good year batteri life also great protect phone side clear skype call long batteri life long rang great hand free devic even take self portrait outsid exterior display cool tri mani mani handsfre gadget one final work well magic help best phone market work well compani ship product promptli work well exactli want great deal excel product satisfi purchas highli recommend encourag peopl give tri better expect great product price sound good reason price effect simpl prompt servic excel product price pleas headset especi love long batteri life work great plantron bluetooth excel buy highli recommend devic everyon jawbon era awesom use sever phone two year one best awesom new look fall 2000 price good use iriv spinn case fit fine overal psych phone appoint contact get great recept everi thing phone work perfectli like realli easi sanyo surviv dozen drop blacktop without ill effect fantast earphon find cell phone right away enter car highli recommend modest price cellular phone get better recept clariti phone awsom devic work great restor phone like new perform would recommend purchas jabra jx 10 seri work flawlessli moto go figur search internet found one best valu happi product screen size big key pad lit well enough camera qualiti excel camera phone glad found product amazon hard find wasn high price portabl work colleagu get great recept littl expens perform great time shipment qualiti product nice solid keyboard well made fit surefir gx2 perfectli would recommend other batteri work great look great strong tmobil servic year pretti happi great softwar motorola deliveri time easi use hold charg long time reason comfort long wear condit qualiti sound tremend keyboard realli worthwhil use sturdi enough don expect problem charg overnight batteri work great won regret love phone pda rock super charg use small hybrid palmtop camera cellphon excel role would definit recommend jabra bt250v look comfort clariti great price verizon year ago realli like servic work describ well im satisfi bose nois cancel amaz import nyc commut batteri excel bargain wear glass fit fine great no problem frog phone charm ador eye catch sound clear peopl talk amaz qualiti got aluminum case new palm vx work realli well protect handheld perfectli far good product well made mobil phone tool must impress job motorola sturdi phone receiv order well within ship timefram everyth good work order overal excit sourc phone sturdi waterproof thank amazon thing need good price end slide edg pant back pocket instead go store bought new nokia phone work great work great nois shield incredi razr batteri good buy keep good work amazon great case price bluetooth rang good day ago left phone trunk got call carri convers without hitch small sleek impress look practic setup ampl storag place eargel channel sound directli ear seem increas sound volum clariti glad purchas no shift no bubbl no peel not even scratch noth couldn happier new one droid jabra earset happi sound qualiti especi outgo better love camera realli pretti good qualiti thank great servic total packag seller understand patient would definit buy great product price purchas famili friend enjoy clariti eas use virgin wireless rock cheap littl phone quit satisfi purchas great new packag nice work good no problem came less time expect great product fix problem happi no troubl access internet download rington perform function everyth descript said would work great car charger especi cannot plug two adapt time play louder speaker size price low would think qualiti lack howev not better expect batteri hold well link phone without effort cool easi use better new pretti good product great phone price amazon excel product would highli recommend research develop divis obvious know phone work fine happi product overal would recommend phone new walkman fit ear well comfort clip belt deffinit make feel like 50 cent come like design look jabra behe ear headset 5020 pretti comfort wear 24 hour day without pain ear amaz quick arriv two origin lg cell phone batteri fraction price work well pair treo 700w skype headset pc use usb bluetooth transceiv great product sure beat use finger case tri normal fall apart month one seem long haul recommend igo anyon differ brand cell phone mp3 player famili post detail comment grey black phone fire red great color not problem item would order need not softwar great job could also make rington form exist cd without internet connect im surpris good qualiti car charger not much review fabul use bitpim free program find internet transfer data phone price cabl excel wind resist not last forev not overli expens replac easi oper sound much better other tri phone sturdi nokia bar phone great phone pleas found product easi set use no ear loop need tini sound great handsfre part work fine car tri download address book treo reboot overal still rate devic high happi purchas good qualiti low price love look feel samsung flipphon well design sharp blue nice color everyth nice sound much less jawbon go replac setup went smoothli well made easi access phone handi detach belt clip work fine motorola 325 cellphon lot better worn batteri volum ringer real good choic loud love rington upbeat easi pair samsung cell work well clear crisp sound use smallest earpiec provid stay pretti well phone also take great pictur even video clip ear occupi background not distract comfort fit need headset comfort least hour time not entir day beat price come strong light use light camera shot even flash so signal serious lightweight work well love cabl allow connect mini usb devic pc cool case open allow screen get scratch appear actual outperform origin batteri china came v325i jabra ear gel best quit work use 18 month purchas anoth one best headset ever own great phone good valu work fine power via usb car wall outlet phone doesn slide around car grip prevent phone slip hand simpli best bluetooth headset sound qualiti span hour two peopl exclaim whoa new phone tv alway use cord headset freedom wireless help case pass one year mark show sign wear 100 function leather nice soft fit tight cut face good shape everyth need couldn ask product great make work lot easier go copier wait hold someth mobil best custom servic anywher arriv quickli much less expens other sold provid good protect look classi krussel alway good tracfonewebsit user friendli make easi toactiv etc design good great recept time would highli recommend product fantast buy get whatev next phone blueant supertooth hand free phone speaker awesom item arriv quickli work great metro pc samsung sch r450 slider phone soni premium sound ear plug light plenti batteri capac confort wear somewhat extend period time blue ant easi use cheap hey work pleasantli supris given low cost item conveni simpl use get job done make car ride much smoother sound qualiti excel well best headset use around year carri highest qualiti anti glare screen protector found date want great product crisp clear recept gener good excel sound batteri life inconspicu boot great bluetooth highli recommend case worthwhil great deal simpl use like great tool entertain commun data manag oh sure use activesync optim data synchron result case would order anoth great phone bought batteri coupon amazon happi purchas perfect ps3 five star plu plu good qualiti bargain bought bought cheapi big lot sound aw peopl end couldn hear good work fine price great deal great price ye shini front side love recept headset excel thumb seller verizon lg phone work well togeth good recept rang exce 20 feet line sight work comfort batteri charg life quit long jabra eargel fit ear well product exactli describ get signal verizon phone won would recommend plan order said phone camera nice mani cool effect play video decent well satisfi cell phone day one keep get better better third one numer palm ship time also quick good case realli good product look nice excel tool especi pair phone auto answer read review haven problem comfort hand work far work like charm week far good case best part download pictur laptop use ir even send pictur laptop phone would recommend item choic headset year great sound good volum good nois cancel nice case feel good hand realli good far probabl best bt headset ever work fine receiv headset good time happi pleas see could replac well travl swivel holster blackberri flip sister one also love good qualiti excel dual purpos headset excel seem complet secur hold belt keep iphon insid good thing fit comfort small ear pair iphon could not happier far best headset ever loud glad say plantron 510 maintain flawless connect cell no static normal use fit comfort came three size earbud tip excel product price quit comfort ear great phone pair two devic easi bare took coupl minut start make call voic dial featur cutout button place perfectli love abl use one headset land line cell despit flaw case except qualiti well worth addit cost own offici oem product hear drive car usual don even put loudest set seller ship quickli much cheaper competitor good show samsung bought 10 save alot money leopard print wonder wild work much better hard plastic case happi far cool phone work good motorola final got voic qualiti bluetooth headset right great would realli recommend get extra minut carri call not get cut headset fulfil requir happi purchas logitech bluetooth headset 10 like fact rest lightli ear rather insid lot websit rate good phone tri cabl comput ipod work fine easi turn car volum control quit access mine year christma bought rest famili otherwis easi instal use clear sound nice leather joy use satisif phone get 2005 bought s710a say wow read spec alon make say wow love great armband realli like product motorola allot clearer ear piec mic exchang soni ericson z500a pretti happi decis impress price case also make easier hold use product motor control center lot high voltag hum equip work great pretti easi beauti style though excel product best charger seen market yet sweetest phone oh charger seem work fine fit secur ear hook not even need use sound better direct ear canal best scene movi gerardo tri find song keep run head saw movi today thought good effort good messag kid love cast jimmi buffet scienc teacher babi owl ador movi show lot florida best made look appeal song best muppet hilari cool right case movi deliv everyth almost right face review long overdu sinc consid tale two sister singl greatest film ever made put gem movi term screenplay cinematographi act post product edit direct aspect film make practic perfect true masterpiec sea faux masterpiec structur film easili tightli construct histori cinema think no film someth vital import occur everi minut word content level film enough easili fill dozen film anyon right mind ask anyth movi quit simpli highest superl form cinema imagin ye film requir rather signific amount puzzl solv piec fit togeth creat beauti pictur number one best th game seri deserv strong love insan game massiv level massiv unlock charact massiv game wast money game kind money wast properli actual graphic good time say canada fun game aye game rock buy play enjoy love pure brillianc film succe despit perhap obvious meagr budget glad film didn go obviou choic lesser film certainli would addit one love song ever written french cancan also boast one cutest lead ladi ever grace screen hard not fall head heel love girl excel cast stori line perform total believ ann hech utterli convinc sam shepard portray gung ho marin sober sat rivet tv screen give one resound 10 think tom hank good actor enjoy read book children littl gener great thing suspens builder good cross line pg especi like non clich choic parent movi could predict dialog verbatim write movi made better select want movi not gross give chill great choic alexand nevski great film amaz film artist one import whoever live first movi given 10 year ever movi need word mouth promot overal film interest thought provok plu well pace suit rel short run time give one look gave 10 wind lion well written superbl act true classic actual turn pretti decent far list horror suspens film go definit worth check manna heaven terrif film predict unpredict time scene often funni occasion touch charact evalu live go cast veteran actor nostalgia trip ursula burton portray nun touch funni time make fun nun church look movi terrif cast good music includ shirley jone rendit way look tonight uplift end give one tri don think disappoint thing realli worth watch sceneri hous beauti term write fresh bold act help write along well mayb idiot savant sister could play better real joy watch applaus given prelud howev realli like great film great director movi edg seat made somewhat afraid go car end night music film realli nice advis anyon go see brilliant 10 10 like movi way much rock world certainli must see anyon no social physic outlet definit cult classic well worth view share other also real treat see anthoni quinn play crazi hors still like movi empower women not enough movi like one excel perform ms garbo show right bat talent could carri silent era want see silent work netflix doesn seem stock also great see renown silent screenwrit franc marion hasn miss step go silent sound love movi receiv great qualiti film age john wayn incred job young movi industri screen presenc shine thought even though senior actor screen think must see older john wayn film jami foxx absolut ray charl perform simpli geniu own film spacek own coal miner daughter quaid own great ball fire fact hard rememb part ray charl act not play man ray charl legendari ray charl life provid excel biograph materi film goe well beyond anoth movi musician hitchcock great director secondli hitchcock pretti much perfect thriller chase movi first watch movi 80 love total fascin music danc everyth good intent made film great film might one greatest film ever made film great actor master director signific theme least would signific theme underton fifti existenti world weari aerial scene ought thrill sens imagin charact one might deepli care yet enjoy watch nice funni funni spoiler whatev els said surfac superbl craft cinematographi simpli stun say least fx noth not state art conceptu show offer littl bit everyth everybodi parent kid fantasi fx fan wasn singl sour note struck act wise either surprisingli solid cast thing consid job well done thank good movi like done releas one best mexican movi ever one less understood even mexican no matter identifi felt rank highli great noir crime drama incred perform belmondo lino ventura attent given everi charact complex psycholog portray detail loyalti treacheri love hope tremend excel drama excel thriller excel film best melvil everyth film simpli incred truli take journey eye soul child black water thriller manag complet transcend limit indi flick continu subvert expect emerg intens experi perform real grip crocdodil extrem well done inde black water websit believ use real croc swamp locat fabul thoroughli enjoy christoph eccleston took control tardi continu seri lot beauti place amaz final possibl best trilog time kieslowski never ceas amaz one favourit director one talent director histori cinema use colour french flag three film noth short incred everi shot everi scene like work art three visual appeal movi ever seen subtl connect three film awesom mention huge spoiler love end charact three film remain survivor ferri disast valentin young judg togeth old man watch tv solidifi happi suffer dealt mani year couldn think better way end film smile face great way wrap amaz film trilog recommend everyon love film movi anyth work art 10 10 movi trilog think one peopl found anoth one roth pearl perform award stun stori told eloqu franci ford coppola 25 year earlier realli unfold gradual leav room charact develop wonder locat lie stori contrast beauti direct film nicola roeg wih sublim cast fun funni movi highli entertain angl featur outlandish array memor psychot lovabl nut enjoy get see run around play game danger even sweet moment telephon repair man realli funni reaction nut bitchi boss truli genuin charact film tremend well play realli find funni actual smart movi brilliant twist end love end perfect shed tear thrill see movi like never forget not cult cult classic realli good show watch educ children year old barney definit super de duper pretti funni babi bop cute kid cool would give televis seri 10 plu could writer smack think best actor actress bonu show charact real must say tape episod find watch know gave 10 don know exactli movi latch endear movi becom special part famili memori total absolut recommend movi anyon like good wholesom famili movi exactli thing four kid get absolut hilari watch one best movi show strong sibl bond much interest action suspens less unneed controversi everyth act cinematographi solid definit worth check damian talent versatil mani way write portray differ charact screen movi cut edg pleas know modern day geniu let passion art drive take us audienc elia kotea jack palanc play good role angelina hot get nake billi drago appear cool usual cameo sven ole thorsen help make enjoy movi good act decent budget awesom movi bought ebay realli love stori line poler bear kinda cute anyon question fort steel ask away think wonder part liter full wonder excerpt work set especi design work camera amaz styliz beauti effect could use exemplar set design stori power explor natur man art watch film want learn work artist highli recommend movi anyon interest art poetri theater polit japanes histori wind lion see wonder render america imperi age make stori differ terrif product valu faultless photographi composit edit terrif cast underappreci brian keith play bulli teddi vivid histori though wind lion told larg eye son everi member famili identifi one charact whether sean conneri nobl brigand candac bergen feisti heroin john huston wili john hay steve kanali spiffi radiant ruthless lieuten roosevelt big stick high adventur best think robert ryan best film portray someon like father schizophren real life father although never murder anyon affect second world war made wors humour get get day apt mother brother saw robert ryan portray type man good imit type individu impress non linear narrat thu mani flashback everi part articul quit well good cinematographi also make monica bellucci look beauti good commentari today love undoubtedli film worth see peopl first timer film make think excel job popular cinema good hous good reaction plenti laugh feel good film felt came cinema northern humour posit commun repres rather enjoy like realli creat uniqu feel though vivian schill excel job script world better 95 garbag theatr today role play well not screami not masculin right camera realli like movi would cast role readi script european movi nice throwback time student 1980 experi live abroad interact nation although circumst slightli differ movi excel angel beauti scamp ador littl yelp he scare funniest part scamp caught curtain angel scamp sing ive never feel total recommend movi come special edit june 20 cover scamp garbag angel underneath lid cant explain movi romant charm hilari ador junkyard scene funni junkyard dog someth special funni laugh kid love buy come new featur interest premis billi drago alway good danger nut bag side note love see drago stephen mchatti lanc hendrikson flick togeth talk rage cheekbon soundtrack wasn terribl either still set got big 10 oy vey scale good job quit amus last 15 minut movi also not bad well like armand assant cabl compani summari sound interest watch twice alreadi probabl believ screenwrit good job tie loos end 10 score mostli plot won say don like spoiler don want one believ film worth time deepli impress charact play knew saw film great thing come gift actor love movi great portray famili share up end knew special love seen mani movi star jaclyn smith god one best though came 12 year ago movi contain star cast love open eye see actor haven seen movi kind long length enjoy everi minut overal rate movi 10 10 scale lifetim not air enough anyon know store sell let know must total differ load understat black comedi film get see rememb movi creat univers fascin everi way movi realli got see realli hope team behind movi make movi continu kinda weird style forgot cast superb trond fausa aurvåg perfect role bothersom man doesn understand movi good humor comment life 2006 found move beauti enjoy uplift initi local site film film buffalo intrigu later found lost power film entir audienc applaud conclus film left theater lilt step joy heart hope human race duri wholesom appear give fine perform rest cast also play well cinematographi noteworthi includ fine view barcelona fame gaudi tower cast good worth check cast great steve martin deliv realli funni perform middl age upper class uptight white guy add betti white jean smart great cast everyon play part realli well steamboat willi amazingli import film cinema histori second appear mickey mous follow silent plane crazi earlier year probabl famou film mostli ground break don yet hear mickey speak ton sound effect music throughout film someth take grant huge crowd pleaser 1928 howev see short 25 year amaz timeless film actual ador see mickey play turkey straw highli imagin occasion cruel way clever real crowd pleaser film still rank among mickey best film even 80 wonder year origin bodi soul 1947 masterpiec john garfield ann rever lilli plmer william conrad canada lee film one greatest cinematograph ever grace screen jame wong howe okay like consid fair critic give credit credit due creatur effect actual pretti cool gotta love close up slimi drool teeth film sole bright spot jonah hill look almost unrecogniz fan recent superbad due amount weight lost interim 90 child truli enjoy show proudli say enjoy big time even classic wb cartoon tini toon kept 90 vibe deliv one popular funni underr cartoon ever creat memori murki say enjoy everi singl episod product relat show easili none cartoon made laugh tender way get dark sitcom orient teenag charact funni peculiar not true lead charact everi singl charact hilari deserv call lead perabo nice energi level obvious comfort front camera believ pitch black done well charact interest want find longer movi goe think peopl surpris doesn make go watch funni charact interest realli care instant classic great soundtrack catchi song end credit don miss master piec film make mani theme simmer occasion boil wart studi poet bohemian self indulg wartim year span aerial bombard london outward tranquil welsh coastal retreat borderlin friendship lust love dedic art experi versu practic concern jealousi rivalri cowardic egot versu heroism self sacrific matur subtl script suggest occasion bring dramat focu underli tension well serv perfect perform apart odd inappropri smile keira knightley prone though perhap direct time charact often mention exquisit visual composit moment invent eleg use close camera angl light includ pointillist faux home movi footag wonder joy behold continu beauti look bertolucci relationship convinc narr engag master work saw film christma great film thought errol flynn brilliant custer sinc becom favourit actor act alongsid olivia de havilland brilliant end fantast alway known errol flynn brilliant actor dad favourit actor grew watch film child wasn watch film realis great actual give film 10 10 love littl thriller hitchcock lot nice shenanigan surround murder spi kidnap child nasti church foreign plot random taxidermist jimmi stewart ever great hero hitchcock stori rip along cool climax embassi function lack brood menac hitchcock black white low budget origin nevertheless yet anoth wonder film great master stabl say film excel produc top rate typic sci fi film excel produc one sci fi best produc scot vandiv entertain nonetheless campi sort film joy watch bare bore moment film plenti humor part cast alway entertain usual overal delight movi excel job reveal complex task incred challeng face south africa believ everi one see movi think peopl outsid south africa understand past attempt truth reconcili process noth short magnific photographi cinematographi film act fantast stori seamlessli woven togeth dog splendid must rent view see don afraid subtitl worth littl avers therapi 10 10 excel perform still good actor around also great direct photographi true shakespear must shakespear fan macbeth jason conneri move tear final monolog brief candl gave sphere moral decay dark forc human face make interest helen baxendal credibl ladi macbeth cheerful time sometim look like naughti girl deadli tast blood evil love death decay shakespear lyric one witti delight adapt dr seuss book brilliantli anim upa finest thoroughli deserv academi award special mention made superb music score sound effect integr element help make memor enjoy cartoon howev paul schrader inde made film mishima superb complex also notabl john bailey fine crisp beauti color cinematographi great product design costum eiko ishioka went memor costum coppola dracula receiv well deserv oscar watch howev small consol actress play ann sister wonder job role stun movi raw sublim move felt like grip intellig stage play without overli theatric feel one actual get watch peopl stage play everyon terror white lie escal monstrou consequ main player mesmeris tom wilkinson broke heart end everyon els judg amount fumbl hanki hand go face among male femal alik julian fellow triumph nation treasur go see excel film aerial scene well done also right balanc war love film give mean phrase never histori human conflict much owe mani kri kristoffersen good movi realli make differ good film great director definit one better documentari seen look famili relationship marriag great movi 10 10 star punish park brilliant piec cinema shot southern california desert use patent faux documentari style watkin creat film like no one peter watkin access film advis look wont regret highli recommend stanwyck morgan perfectli cast mani way modern equival dicken christma carol sensibl success film depend cast sydney greenstreet alexand yardley charact attract set use throughout film eye pleas gem transfer good nice relax late night view movi full wonder danc henc titl great music terrif sceneri handl tough issu digniti grace cours shock spoiler happi wonder feel good end point journey journey touch everi member famili one movi need see whenev come tv never mind fact alreadi memor saw film recommend good plot hold complet attent act superb tom wilkinson fantast emili watson also good good film inde great uncondit love tom wilkinson charact man not prepar ordeal begin take matter hand stori progress great actor give perform make feel charact anguish suffer emili watson charact strong give quick glanc understand everyth despit pan review like movi fact like better interview vampir like lestat stuart townsend better cruis attempt aailiyah pretti good akasha place compel first entranc mini danc scene big fan seri mostli due ann rice style sensit treatment guess like detail dysfunct believ thought act skill meredith better right charm film wonder sentiment heart rare film maker take time tell worthi moral tale care love doesn fall trap overli syrupi indulg nine ten truli love film earli film futur goremeist lucio fulci good addit giallo sub genr one best italian thriller earli 70 standout scene technic film well made impress camera work solid act effect music riz ortolani particularli good recur unaccompani femal vocal sound like come distant hill still wild stuff though highli recommend fan giallo cinema movi interest begin end like way dustin hoffman charact readi everyth stay son movi also reveal person think show peopl learn find compromis self without involv peopl issu ps scene movi cool central charact find room blown fresh subtl rather sublim effect italian review call small great film right actor give wonder perform especi jennif rubin jami harri chang nervou starlet begin strang event part cool star learn lot real insid emot peopl movi lot movi busi movi movi situat begin game play act coach fascin also music mark snow possibl best score ever heard won forget movi saw child tv back 1973 stranger love duet astronaut doctor begin movi perfect exchang one consid movi made well cold war astronaut biggest fear crash ussr movi awesom love realli scari love scream movi horror movi one rank way want real scare rent one 10 10 extraordinari film courtroom drama compel indict american justic system frighten film highlight fundament flaw legal process not discov guilt innoc rather present better court film well pace underst one best courtroom documentari seen mostli routin fact base tv drama get boost fine perform cole predict not bad watch clear rang abil pull part carri movi well constantin give everyth right intens seem good understand underli psycholog motiv wonder inspir watch hope get releas video dvd edit edit film phenomen opinion song could explain emot subject better jay adam unfortun life subject talk song old man neil young play evok mani emot cinematographi film shot interest way cours footag 70 graini enhanc film film offer mani delight surpris achil philippa beauti sing duet don giovanni perfectli describ situat movi appreci subtl layer excel film stori unfold 18th centuri jutland use period music play period instrument one fine touch share gener loewenhielm exquisit joy partak caill en sarcophag even though watch movi wish small sampl savor underst film leav last impress warmth gener contrast auster backdrop leav theater want go danc star act expect cast top notch charact flesh surprisingli well particularli grime blake actor deliv sharpli script line right amount deadpan tongu cheek make dialogu hilari realist angu scrimm also turn good perform somewhat brief memor role gentli menac violin play anatomist doctor quinn conclus love long time sinc entertain movi struggl find anyth bad say mark word one cult film like evil dead phantasm peopl still discov fall love 20 30 40 year line pretti surpris wonder film made 1949 hollywood gener collect head sand concern black white issu time film deserv strong kudo take stand except act mostli lesser known cast super intellig script doesn insult audienc take easi way come white racism plu movi rather modest budget fast run time amaz job juano hernandez except actor play support role mani film era proud black man accus murder white man south trumbul love luci see kid chanc open amaz dialog far race relat come last 50 year see film year howev remind us huston still top game evinc faith adapt jame joyc acclaim novella dead feel thought gabriel discomfort danc intang leap life come within viewer grasp huston portray movi possibl one creativ work horror ever everyth could want suspens drama comedi confus subplot nativ american brain eat look end brainsuck movi look no great sound effect impress special effect recommend movi enough call nut think one best movi ever great charact actor telli savala peter boyl won spoil end pretti amaz best scene movi end won spoil ever indic writer director abil meld two highli volatil tempera seamless union creativ result powerhous achiev made time perhap cultur disturb fascin celebr distort interpret fame film not easili forgotten kevin spacey excel verbal tsunami buddi ackerman total believ great actor script subtl comedi unmatch movi recent year charact interest even bit predict highli recommend age although younger set probabl not appreci subtl refer certainli appreci one galley scene particular great movi linda cardellini thing good film pois amaz fact film wonder heartwarm tale two peopl chase dream best part nurs betti unpredict director neil labut use brutal violenc seper dream realiti along touch drama hilari comedi never tell go happen next howev pretti good act least type film saw short film hbo day absolut love didn realiz wonder short realli last two scene excel short film hope director jame cox turn short featur length film cast win us whole new film not much dialogu not much music whole film shot elabor aesthet like sculptur funni clever hip like pray previou film hype long time didn see charismat actor screen paolo sorrentino written wonder stori loneli toni built one unforgett charact seen movi recent year movi not complet perfect titta di girolamo stay long time vision movi rate movi 10 not know emilio estevez directori debut pace interplay develop charact well clever camera work surround charact estevez play suggest natur eye interplay martin emilio contain wonder chemistri saw wall street martin charli kathi bate wonder charact subtl desper escap variat charact play field lord reader alreadi seen one miyazaki film still top form made anoth worthwhil experi never condescend charact good genuin heart believ problem two main charact may two believ children ever saw put screen easi love even easi identifi movi great especi enjoy visual art sceneri two daughter paint photograph beauti stori also funni poignant time peopl like european film art movi like movi truli art movi actual lot art go rent howev final watch film realiz not close mind brillianc depict also found watch one movi never ceas interest simpli keep alert tri attempt deciph mean brillianc inde like movi like matrix better yet sequel think appreci thought provok mindblow experi film give think film like dream simpli beauti rickman stow play role hilt tale children book writer mayb written subvers tract glorious fun fast pace fairli accur portray night raver present idyl yet seriou portray up down charact live pick speed got right point show exactli govern scientist argu human reason gadget enjoy recommend friend particularli pleas act abil dwight schultz actor truli understand becom particular charact deliv convinc sincer perform screen chemistri critic entir film genuin film dialogu natur real life writer gorman bechard undoubtedli homework refer industri charact age appropri incred soundtrack truli captur essenc film track command sentiment actual contribut scene charact definit worth see sort thought provok film forc question threshold loneli hayao miyazaki latest eighth film studio ghibili gake no ue no ponyo ponyo cliff sea wonder fun imagin look childhood time seem film anim domin disney pixar cgi masterpiec refresh comfort know miyazaki still reli tradit hand drawn anim tell charm enchant stori enough not said remark anim film art style appear crayon pencil draw wonder color fanci still make super end depict great sea vessel taken mighti frost consid excel stori solid act look film ad bonus watch two sunday ago march 20th 2005 began realli enjoy time tape entir thing well act done tv movi judith light one favorit actress think superb job film keep watch sad movi good not seen movi definit recommend love usual cuti still quit interest entertain follow recommend confid movi well balanc comedi drama thoroughli enjoy riot see hugo weav play sex obsess gay real estat salesman use client hous tryst flame darren tom holland anyway plot flow smoothli male bond scene hoot open sequenc gem classic cat mous game follow delight watch fan genr heaven lang becom great actress look like wonder stori wow love place stop late may bank holiday rick steve recommend love select menu great price fri great great touch servic prompt tri cape cod ravoli chicken cranberri mmmm highli recommend food amaz servic also cute could care less interior beauti perform right red velvet cake ohhh stuff good hole wall great mexican street taco friendli staff also combo like burger fri beer 23 decent deal found place accid could not happier overal like place lot redeem qualiti restaur inexpens ampl portion good price first visit hiro delight shrimp tender moist posit note server attent provid great servic thing like prime rib dessert section burger good beef cook right want sandwich go firehous side greek salad greek dress tasti pita hummu refresh order duck rare pink tender insid nice char outsid came run us realiz husband left sunglass tabl chow mein good portion huge love friendli server great food wonder imagin menu salad right amount sauc not power scallop perfectli cook place receiv star appet cocktail handmad delici definit go back glad found place great food servic huge portion give militari discount alway great time do gringo updat went back second time still amaz great way finish great deal includ tast drink jeff went beyond expect realli realli good rice time sweet potato fri good season well today second time lunch buffet pretti good place love pho spring roll oh yummi tri say food amaz omelet die everyth fresh delici like realli sexi parti mouth outrag flirt hottest person parti best breakfast buffet back food arriv quickli side cafe serv realli good food server fantast found wife love roast garlic bone marrow ad extra meal anoth marrow go good thing waiter help kept bloddi mari come best buffet town price cannot beat love mussel cook wine reduct duck tender potato dish delici one better buffet went tigerlilli fantast afternoon food delici bartend attent person got great deal ambienc wonder music play go back next trip sooooo good realli fantast thai restaur definit worth visit nice spici tender good price check servic fine waitress friendli guy steak steak love son steak best worst place said best steak ever eaten thought ventur away get good sushi place realli hit spot night phenomen food servic ambianc definit worth ventur strip pork belli return next time vega penn vodka excel good select food includ massiv meatloaf sandwich crispi chicken wrap delish tuna melt tasti burger delici nyc bagel good select cream chees real lox caper even great subway fact good come everi subway not meet expect serious solid breakfast one best bar food vega drink never empti made realli great menu suggest nice blanket moz top feel like done cover subpar food bathroom clean place well decor fiancé came middl day greet seat right away great restaur mandalay bay highlight great qualiti nigiri staff friendli joint alway clean differ cut piec day still wonder tender well well flavor order voodoo pasta first time realli excel pasta sinc go gluten free sever year ago place good seat immedi menu divers reason price restaur alway full never wait delici place hand one best place eat phoenix metro area bacon hella salti realli vega fine dine use right menu hand ladi no price list waitress friendli lordi khao soi dish not miss curri lover everyth menu terrif also thrill made amaz accommod vegetarian daughter atmospher modern hip maintain touch cozi not weekli haunt definit place come back everi love place hit spot want someth healthi not lack quantiti flavor order lemon raspberri ice cocktail also incred interest decor realli like crepe station also serv hot bread butter home made potato chip bacon bit top origin good watch prepar delici food egg roll fantast salad wing ice cream dessert left feel quit satisfi best place go tasti bowl pho friendli staff worth drive heard good thing place exceed everi hope could dream food great serivc great brunch spot servic friendli invit good lunch spot side delish mix mushroom yukon gold pure white corn beateou friend love salmon tartar extrem tasti waitress good though jamaican mojito delici food rich order accordingli love bacon wrap date unbeliev bargain folk otto alway make us feel welcom special place first pho amaz wonder experi made place must stop whenev town fun chef great place coupl drink watch sport event wall cover tv say would hardest decis honestli dish tast suppos tast amaz everyon attent provid excel custom servic dish quit flavour serious flavor delight folk much better ayc sushi place went vega light dark enough set mood owner realli great peopl greek dress creami flavor pizza good peanut sauc tasti tabl servic pretti fast fantast servic know make hope place stick around restaur atmospher exquisit good servic clean inexpens boot seafood fresh gener portion plu buck thu far visit twice food absolut delici time good year ago veggitarian platter world cant go wrong food beat stop place madison ironman friendli kind staff chef friendli good job like patio servic outstand goat taco didn skimp meat wow flavor went bachi burger friend recommend not disappoint would definit recommend wing well pizza great pizza salad place amaz staff great food delish incred beer select didn know pull pork could soooo delici get incred fresh fish prepar care love fact everyth menu worth food excel servic good good beer drink select good food select good servic check summer dine charm outdoor patio delight not expect good fantast food food good great food price high qualiti hous made back good bbq lighter fare reason price tell public back old way consid two us left full happi 20 go wrong bread made hous servic except food good review coupl month later return amaz meal favorit place town shawarrrrrrma black eye pea sweet potato unreal won disappoint could serv vinaigrett may make better overal dish still good truli unbeliev good glad went back fantast servic pleas atmospher love place great servic food omg food delicioso dish salmon best great veget fresh sauc feel like authent thai worth drive tucson pretti good beer select place like chipotl better classi warm atmospher fun fresh appet succul steak basebal steak star brick oven bread app eaten multipl time time food delici everyon treat equal special delici good side staff genuin pleasant enthusiast real treat alway even wonder food delici best fish ever life bathroom next door nice outstand littl restaur best food ever tast pretti cool would say server great job handl larg rowdi tabl would come back sushi crave vega staff great ambianc great deserv star dine space tini elegantli decor comfort custom order way like usual eggplant green bean stir fri love best taco town far interest part town place amaz great dinner servic outshin definit recommend halibut come back everi time vega def come back bowl next time want healthi authent ethic food tri place continu come ladi night andddd date night highli recommend place anyon area sever time past experi alway great walk away stuf happi first vega buffet experi servic excel price pretti reason consid vega locat insid crystal shop mall aria summar food incred nay transcend noth bring joy quit like memori pneumat condiment dispens kid pizza alway hit lot great side dish option kiddo servic perfect famili atmospher nice see cook perfect servic impecc great place eat remind littl mom pop shop san francisco bay area today first tast buldogi gourmet hot dog tell ever thought possibl definit soon food realli good got full petti fast servic fantast don know kind best ice tea come hungri leav happi stuf assur won disappoint realli enjoy eat first time go think quickli becom regular server nice even though look littl overwhelm need stay profession friendli end dinner companion told everyth fresh nice textur tast no complaint serious good pizza expert connisseur topic nicest restaur owner ever come across love biscuit servic quick friendli absolutley fantast definit come back like steiner dark feel like bar wow spici delici not familiar check love go back anyway fs restaur wonder breakfast lunch day week differ deal delici not mention combin pear almond bacon big winner food delici spici enough sure ask spicier prefer way ribey steak cook perfectli great mesquit flavor food gooodd food good enjoy everi mouth enjoy relax venu coupl small famili group etc nargil think great best tater tot southwest love place vanilla ice cream creami smooth profiterol choux pastri fresh enough im az time new spot insid realli quit nice clean food outstand price reason love margarita first vega buffet not disappoint good though great pork sandwich enjoy pizza brunch steak well trim also perfectli cook group 70 claim would 40 handl us beauti love place jewel la vega exactli hope find nearli ten year live delici absolut back isn small famili restaur fine dine establish toro tartar cavier extraordinari like thinli slice wagyu white truffl awesom back mani time soon menu much good stuff could not decid conclus fill meal daili special alway hit group pancak also realli good pretti larg first crawfish experi delici monster chicken fri steak egg time favorit waitress sweet funni also tast mom multi grain pumpkin pancak pecan butter amaz fluffi delici cant say enough good thing place ambianc incred waitress manag friendli don much pasta love homemad hand made pasta thin pizza give tri happi far best cheesecurd ever reason price also everyth perfect night food good typic bar food first glanc love bakeri cafe nice ambianc clean friendli staff point finger item menu order won disappoint oh thing beauti restaur haven gone go burger amaz sure order dessert even need pack go tiramisu cannoli die first time wait next bartend also nice everyth good tasti place two thumb way best place vega breakfast check sat sun love authent mexican food want whole bunch interest yet delici meat choos need tri place excel new restaur experienc frenchman great steak great side great wine amaz dessert steak shrimp opinion best entre gc opportun today sampl amaz pizza yellowtail carpaccio melt mouth fresh spici enough perfect actual last night second time dine happi decid go back boyfriend came first time recent trip vega could not pleas qualiti food servic realli recommend place go wrong donut place nice ambianc would recommend save room tapa dish delici heart place good time believ place great stop huge belli hanker sushi gener portion great tast food delici also came back check us regularli excel servic server super nice check us mani time servic good compani better staff also friendli effici servic fan quick serv nice folk steak 100 recommend great food great servic clean friendli set assur back breakfast perpar great beauti present giant slice toast lightli dust powder sugar great place fo take eat waitress friendli happi accomod vegan veggi option omg felt like never eaten thai food dish crouton also tast homemad extra plu regular stop trip phoenix realli enjoy crema café expand even told friend best breakfast miss wish one philadelphia also best chees crisp town good valu great food great servic couldn ask satisfi meal food good awesom made drive way north scottsdal not one bit disappoint absolut must visit cow tongu cheek taco amaz realli want make experi good one grill chicken tender yellow saffron season pretti awesom place ambienc perfect staff alway super friendli help especi cool bring two small boy babi four star food guy blue shirt great vibe still let us eat roast beef sandwich tast realli good high qualiti chicken chicken caesar salad promptli greet seat proven dead wrong sushi bar not qualiti great servic fast food impecc good joint macaron insan good waiter attent friendli inform great food great place relax awesom burger beer perfect sit famili meal get togeth friend patio seat comfort hand favorit italian restaur scream legit book somethat also pretti rare vega not fun experi atmospher great love duo violinist play song request person love hummu pita baklava falafel baba ganoush amaz eggplant conveni sinc stay mgm owner super friendli staff courteou great eclect select sweet potato tot good onion ring perfect close staff attent chef gener time even came around twice take pictur owner use work nobu place realli similar half price promis won disappoint great doubl cheeseburg awesom servic food fantast neighborhood gem wait go back great place highli recommend gave star give star dessert panna cotta amaz good food great atmospher damn good steak price reason flavor spot sauc home made slaw not drench mayo decor nice piano music soundtrack pleasant steak amaz rge fillet relleno best seafood plate ever good food good servic absolut amaz definit back sergeant pepper beef sandwich auju sauc excel sandwich well hawaiian breez mango magic pineappl delight smoothi tri far good go sinc 2007 everi meal awesom server nice attent serv staff cashier friendli even brought food atmospher fun servic quick even go order like like said mouth belli still quit pleas thumb love grill pizza remind legit italian pizza pro larg seat area nice bar area great simpl drink menu best brick oven pizza homemad dough realli nice atmospher one bite hook cute quaint simpl honest chicken delici season perfect fri outsid moist chicken insid food great alway compliment chef special thank dylan recommend order yummi tummi awesom select beer great food awesom servic one nice thing ad gratuiti bill sinc parti larger didn expect tip han nan chicken also tasti servic thought good nicest chines restaur overal like food servic also serv indian naan bread hummu spici pine nut sauc world tri airport experi tasti food speedi friendli servic love decor chines calligraphi wall paper never anyth complain restaur clean famili restaur feel warm feel servic felt like guest special treat extens menu provid lot option breakfast alway order vegetarian menu dinner wide array option choos wonder lil tapa ambienc made feel warm fuzzi insid got enjoy seafood salad fabul vinegrett wonton thin not thick chewi almost melt mouth level spici perfect spice didn whelm soup sat right time server get go fantast side town definit spot hit great place eat jalapeno bacon soooo good food good servic good price good happi one place phoenix would defin go back block amaz close hous low key non fanci afford price good food hot sour egg flower soup absolut star great time famili dinner sunday night flair bartend absolut amaz good order twice pretti satifi experi join club get awesom offer via email perfect someon like beer ice cold case even colder nacho must don mani word say place everyth pretti well staff super nice quick even crazi crowd downtown juri lawyer court staff great atmospher friendli fast servic receiv pita huge lot meat thumb classic main lobster roll fantast good go review place twice herea tribut place tribut event held last night chip salsa realli good salsa fresh place great get insid impress place servic super friendli place nice surpris golden crispi delici could eat bruschetta day devin lastli mozzarella stick best thing order first time ever came amaz experi still tell peopl awesom duck love place say dessert yummi season fruit fresh white peach pure love put food nice plastic contain oppos cram littl paper takeout box crêpe delic thin moist best servic food ever maria server good friendli made day excel lunch great experi recent tri caballero back everi week sinc food came good pace ate twice last visit especi enjoy salmon salad fact go round star awesom potato great biscuit flavor perfect amount heat price reason servic great went happi hour great list wine may say buffet pricey think get pay place get quit lot place pretti good nice littl vibe restaur talk great custom servic cours back love fri bean alway pleasur deal plethora salad sandwich everyth tri get seal approv place awesom want someth light healthi summer sushi strip place go servic great even manag came help tabl excel restaur highlight great servic uniqu menu beauti set boyfriend sat bar complet delight experi go place gyro love owner chef one authent japanes cool dude defin enjoy serious killer hot chai latt boyfriend tri mediterranean chicken salad fell love rotat beer tap also highlight place stay vega must get breakfast least want first say server great perfect servic pizza select good strawberri tea good overal great experi regular toast bread equal satisfi occasion pat butter mmmm chip sal amaz new fav vega buffet spot everi time eat see care teamwork profession degre\n",
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            "String de los comentarios/tokens filtrados negativos:\n",
            " no way plug us unless go convert tie charger convers last 45 minut major problem jiggl plug get line right get decent volum sever dozen sever hundr contact imagin fun send one one needless say wast money wast money time two seper mere ft start notic excess static garbl sound headset design odd ear clip not comfort advis everyon not fool click place way make wonder long mechan would last went motorola websit follow direct could not get pair commerci mislead bought mother problem batteri didn think instruct provid help peopl couldnt hear talk pull earphon talk phone doesn hold charg simpl littl phone use breakag unaccept unus move car freeway speed two year left contract hate phone need least min get phone book time first turn phone batteri life short poor talk time perform worthless product not impress product hear garbag audio right mind gonna buy batteri argu verizon regard drop call return phone two day disappoint batteri not loud enough doesn turn like disappoint button bad essenti forget microsoft tech support buy differ phone not hold phone particular angl parti hear clearli one big drawback mp3 player button phone front cover let paus skip song lock second week later activ suddenli die mic doesn work even bmw seri fairli quiet troubl hear person say not buy d807 wrongli advertis d807 purchas within day no longer work not good bargain batteri run quickli item work great broke month use construct headset poor product cost much one expect work far better greater eas thing ear bud play music one ear dont wast money plan use car forget found product waaay big disappoint decis buyer bewar could flush money right toilet receiv supposedli new motorola 375 appar not match phone charger huge design flaw unless not use correctli don think case camera although rate impress megapixel render imag fall well expect rel high resolut purcash car charger not work got phone around end may complet unhappi could not get strong enough signal bad qualiti basic servic bad not work cell phone plug set charger bt headset disapoin buyer care major logitech earbud headset fail hous get drop coverag upstair no coverag basement wast sever differ minut call experienc drop area bar not happi item disappoint phone take forev charg like hour liter wast time recept poor item state includ descript not includ month screen went black sudden hope audio qualiti poor poor bought wire headphon sound better whine internet goe use thing less like essenti commun tool not commun still maintain monkey shouldn make headphon obvious don share enough dna copi design human doesn work bougth l7c not work doesn last long one thing hate mode set button side thank wast money audio qualiti poor put latest os v1 15g like slow crawl lock everi recogn phone storag devic like loud buzz overrid convers phone don buy product bluetoooth headset time still not comfort way fit ear phone thorn side realli abhor recent problem could not stay connect 10 minut disconnect design flaw wast 13 buck check voic mail night keypad backlight turn second first messag lost replac die week problem wit hit drop signal not impress bad choic pleather case doesn fit useless phone simpli deaf make strang tick nois end call batteri last hour send back not bother contact compani dollar product learn lesson not bought form onlin anyway hate earbug avoid phone mean also didn like button felt like would crack use worst phone ever freez frequently4 kind embarrass use look mostli embarrass child like compani expect better consum experi motorola horribl tick sound background call never experienc bit disappoint phone year tell not great order soni ericsson w810i think work that first use batteri complet useless big wast time don wast one not make mistak big disappoint calendar sync phone unus not new worst custom servic ever no addit ear gel provid no instruct whatsoev defeat purpos bluetooth headset internet excrutiatingli slow product strong rubber petroleum smell unbear caus return poor qualiti servic cabl look thin flimsi scari absolutel junk one didn work either return hour later don wast money not drain player may also potenti fri unreli give avoid one wast littl money earpiec first doesn wear well wouldn recommend buy product piec junk poorli contstruct hing don buy not purchas phone custom servic terribl batteri life still not long enough motorola razor v3i send back mani peopl complain found headset microphon weak year old nokia 2160 tracfon hold charg lot better not care consum one bit instruct manual lack batteri unreli well servic use antena uncomfort sound qualiti quit poor compar phone razr previou wire headset plug lg want money back order product first unhappi immedi ngage still lack earbud bar cell phone home cant not hear anyth howev riington not best neither game plug not work well amazon suck rip charg ship long last batteri don recharg frequentyli flip phone obvious problem adhes disappoint inexpens piec would still expect better qualiti practic useless not add kind boost recept bought not happi piec junk broke phone day worst custom servic recept terribl full static sit vehicl cradl belt headset lost signal jerk phone network coverag lo angel horribl excel starter wireless headset loudspeak option great bumper light appeal thing think could improv sound leak headset first recept suck never bar ever phone get extrem hot also phone drop case not go save special drop face bad fit way big wast money drivng along headset start ring no reason worst phone ever month not good item work start problem auto revers tape player embarass also ear hurt tri push ear plug ear averag phone bad batteri life oper weak network soyo technolog suck problem other mention worst piec crap ever along verizon custom servic poor sound qualiti pictur resolut far compar price phone offer today use earpiec left right thing not stay ear still wait sure item would work well ever reciev disappoint don wast money time stupid keep buy new charger car charger cradl headphon car kit everi time new phone come came verizon cingulair nicer cell phone first thing notic realli bad servic look new earpiec receiv white color batteri goe dead coupl hoursth origin use last week still last longer thereplac year batteri went complet dead headset first phone cheapli made att not clear sound distort yell talk plastic break realli easi clip don make mistak oh forgot also mention weird color effect phone also area unit broke not fond magnet strap anoth note phone appear realli look rather bland especi black model low qualiti warn stay away wish could return unit get back money use talk hour batteri would liter drain die don find bad unit come one earpad would not recommend anyon displeas might gotten defect would not risk buy built qualiti alon made case small difficult instal not fit realli noth bad say headset even fulli charg went bed turn blue tooth wi fi notic 20 left morn also problem phone read memori card alway turn not good wear hat sunglass horribl phone bt50 batteri junk cant get softwar work comput disappoint poor voic clariti no buyer remors one disappoint accessoryon find inexcus probabl return phone perhap chang carrier updat procedur difficult cumbersom disappoint hate anyth goe ear uncomfort ear don use lg vx9900 env volum switch rocket unit destin unknown like return verizon bill howev difficult understand even though price plan simpl disappoint not user friendli unfortun abil actual know receiv call rather import featur phone piti respect exchang phone even problem result phone either stuck max volum mute pretti piec junk plug wrong size save money item 11 month bad purchas defect crap would given no star abl sound qualiti devic unaccept unless realli quiet area almost hear noth would not recommend item anyon holster arriv not match photo ad earlier review note plug charger noth happen never know push hard enough right number time function want not not impress would not recommend item anyon complaint standard sound volum littl low even turn realli ugli horribl switch time no real improv refus refund replac unfortun easi accident activ gentl touch button accident touch phone face listen basic threw money window noth took one drop inch kitchen counter crack not impress not laugh none new one ever quit work properli miss numer call reason perhap phone defect peopl cannot hear use don buy product suck stuff not work phone worst softwar ever use could give zero star would tri make call exercis frustrat case flimsi piec plastic no front side protect whatsoev give wirefli star contact cingular inform practic aggrav none work don buy muddi low qualiti sound case around wire insert poorli super glu slid pleas dont wast money look good qualiti motorola headset keep look isn yet plantroninc continu use flaw charger design disapoint result cumbersom design piec crap experi terribl fourth bluetooth headset much comfort last jabra hate worst ever bare ever hear constantli say batteri life highli unaccept compani joke due happen everi call forc stop use headset verizon tech support walk procedur none work end hard set wipe data voic qualiti signal strength drop call feel poorli construct menu difficult navig button recess difficult push also difficult put recommend avoid product use two day wasn alway easi hear got car charger not even week charger broken went plug start smoke sprint terribl custom servic posses get junk no idea piec trash real killer volum cours break poor construct infuri sprint charg servic cheap feel look cheap doesn work europ asia worst phone samsung crap crappi samsung e715 worst ever seeen charger work week complet stop charg phone poor product howev need better instruct need two hand oper screen softwar interfac decad old cannot compet new softwar design steer clear product go genuin palm replacementr pen come three pack not buy not buyit suck plu serious not believ worth steep price point dissapoint perform don make mistak pleas don buy phone phone might well worst ever brand case less extra origin put later discard scratch ear don bother go store get pay guess howev bt headset current not good real time game like first person shooter sinc audio delay mess disappoint accessori good manufactur perform aw muffl tinni incom sound sever echo end call obvious terribl custom servic get pay contact compani told although unit still warranti couldn produc receipt luck linksi way exchang bad phone refurb unit someth place treo case not not snug lot extra room side batteri terribl earpiec larg heavi keep fall ear utter crap sound qualiti terribl charger arriv within promis timefram not work final three four time spring latch broke could not use longer visor talk useless custom servic felt light tinni not fit palm tungsten e2 broke first time tri plug could not recommend look good pictur case huge disappoint extrem slow take forev anyth must studi anyon interest worst sin industri design first thing happen track continu pair period sinc somehow kept drop upload rington third parti bluetooth not work phone lock screen flash make call randomli pocket lock truli aw none tone accept camera color balanc aw product clearli not readi prime time don care cool look tell call come worthless chines forgeri abound instruct didn explain microphon jack could use would advis not purchas item never work well bad pay month servic nokia ca 42 usb cabl not work phone biggest complaint batteri drain superfast design might ergonom theori could not stand ear not buy want use holster doesn make look cool also phone doesn seem accept anyth except cbr mp3 prefer rip window media player bewar anyon near hear part convers bought phone replac startac regret sinc store anyth phone number sim last less 3o minut actual tri use phone wife phone problem poor qualiti not worth phone crash complet get replac unfortun not recharg iphon 4s despit connect multipl power sourc imac extern batteri wall outlet etc simpli want small flip phone look elsewher extra bell whistl mediocr think plan one sure last moto phone receiv use product day broke none three size sent headset would stay ear tri unit fail within month pro couldn use unit sunglass not good texa dit not work time nokia 5320 bought mainli charger broke soon purchas like loud buzz overrid convers phone stay away store care not fit disappoint camera phone may use dustpan indoor rather use dispos day later puff smoke came phone use nano state son dissapoint would not reccommend particular model would not work motorola smartphon wont work right atleast addit feel amp look phone lightweight cheap plastic warranti problem reoccur bottom line put money somewher els cingular not support realli want plantron 510 right one mani issu good first charg kept go dead 12 minut cheap plastic creak like old wooden floor get absolut horribl recept apart phone not problem phone got year old slowli break despit much care part sorri made purchas bought hope could make bluetooth headset fit better thing made imposs wear treo mobil refus replac forc buy anoth phone without kind upgrad discount horribl don think would hold securli belt allow possibl doubl book date time first work week break use case smell disgust not want internet access fine rare instanc work 50 drain tri talk real loud shout telephon get old still told wasn great wind complet useless not work first thought grtting good deal 44 plug phone v3c razr improp descript return batteri no life check everywher no featur realli disappoint doesn job awkward use unreli not good hope doesn work unfortun not work three broke within two month use dont buy father v265 batteri die howev ear pad come easili one week lost one call drop phone come screen goe black worst stop ring intermitt horribl horribl protector don like nokia either reach bottom row uncomfort send end key not expect bought nokia cell phone week ago nightmar would take long describ bad custom servic experi amazon disappoint thing no speakerphon terribl car not accept cassett product useless sinc not enough charg current charg cellphon plan use not say stay away use dirti unreli servic mobil not good enough price bought two neither charg product cover import light sensor ear outlet not work poor reliabl take pictur batteri drop bar start beep let know die terribl product wast money last time buy wast money item receiv counterfeit far worst purchas made amazon not work listen music cingular 8125 bottowm line anoth worthless cheap gimmick sprint troubl volum warn not buy open batteri connect broken devic turn didn want clip go top ear caus discomfort don trust websit don expect help support speaker low qualiti make ring tone sound cheap bad recept product high qualiti chines crap don buy product fail everyth product wrong first utterli confus first caus lose coupl import contact terribl phone holder problem ear loop made weak materi break easili not nearli good look amazon pictur make look stupid alway cut make beep beep beep sound say signal fail couldn figur phone sound ok not great ok wife phone almost total unintellig couldn understand word said take batteri phone put back togeth restart manag bend leaf spring back place metal enough stress break next drop saggi floppi piec junk also new problem loos connect phone even within coupl feet two make easier keep bluetooth not wear said pretti useless never got pair headphon worst ever sound wise drain weak snap not charg cingular att 8525 phone howev year fliptop start get loos wobbl eventu screen went black couldn receiv place call kept catch cabl seat pull phone turn mic joke volum quit low return not maintain connect comput lap purchas differ case much disappoint compani didn charg button small howev keypad tinni sometim reach wrong button cannot make call certain place howev girl complain time phone doesn wake like normal phone compani charg restock fee still not given refund back tri return refund plug find not darn thing work earbud piec break easili lousi product phone tri hard everyth fail abil phone not enough volum echo problem unsatisfactori could take video time qualiti poor don wast money go first neg review product adapt not provid enough charg current much hype phone assum best mistak also cannot take pictur case lens cover phone fall easili didn work peopl not hear talk text messag featur realli tricki use realli disappoint charger doesn work pain ear last one day blew disappoint kind flop around screen get smudg easili touch ear face piec junk lose call phone item not match pictur thing disappoint infra red port irda not answer call unit never work slow move aimless movi distress drift young man not sure lost flat charact audienc nearli half walk attempt arti black white clever camera angl movi disappoint becam even ridicul act poor plot line almost non exist littl music anyth speak rest movi lack art charm mean empti work guess empti wast two hour bit predict averag act main person low budget clearli see short film certainli pull no punch graphic far best part game today graphic crap flick doom concept idea lame take minor charact mediocr pg 13 film make complet non sequel chang tone pg rate famili movi wasn least bit interest not confirm film would unfunni gener also manag give away entir movi not exagger everi moment everi plot point everi joke told trailer not funni even talent carrel save co star don fare much better peopl like morgan freeman jonah hill ed helm wast stori predict lazi real effect work presenc anim integr scene worst obviou blue green screen work ever seen whatev cost much didn translat qualiti sure neg insipid enough caus regret anoth hour life wast front screen long whini pointless recommend wait futur effort let one go disappoint movi one charact total annoy voic give feel fingernail chalkboard total unnecessari train roller coaster scene absolut no warmth charm scene charact movi total grate nerv perform not improv improvis actor twice much worri not whether deliv line well whether line good quit honestli often not good often dialogu doesn realli follow one line anoth fit surround crackl unpredict youth energi honestli found hard follow concentr meander badli wouldn say worth hour time though glad pretenti piec didn plan dodg stratu big shot gonna help movi maker aren restrain movi busi québec problem script horrend noth believ suspens feel frustrat retard girl frankli cotton club unfaith kind embarrass watch lane gere film bad act bad dialog extrem shallow insincer predict even chick flick polit correct disappoint love lane never seen movi lousi hour half wish could bring back direct cinematographi aren quit good movi bore sometim found occupi peak paper instead watch never happen columbo movi sometim embarrass look away direct seem pretenti scene oh matur neighbour girl misplac gener line plot weaker averag episod scene debat whether not sack trumpet fals accus murder pure horror realli stupid problem thought actor play villain low rent michael ironsid howev didn make fact overal tremend bore movi no chemistri ben affleck sandra bullock film couldn understand would consid even leav wife chick supposedli knock sever moment movi didn need excruciatingli slow move poor remak best friend wed great disappoint cannot believ actor agre film could not stand even watch long fear lose guess nobodi network air dribbl watch put imdb rate go low aw time get neg number case saw mirrormask last night unsatisfactori experi unfortun inexperi direct meant scene scene pass littl way dramat tension conflict central theme film handl ineptli stereotyp no depth imagin pretti pictur world cannot make piec work flaw core hour half wast time follow bunch pretti high schooler whine cri life relat hell bare understand movi pure disast stori stupid edit worst seen confus incred fish badli made underwat shot repeat thousand time film truli truli bad film act terribl write wors possibl way movi could redeem would mst3k fodder paid much bore no plot whatsoev no plot horribl worst hour half life oh gosh walk theatr minut get relief hate movi like yeah movi pretti much suck no plot storylin go see movi bring pillow girlfriend boyfriend keep occupi aw don think ever gone movi dislik much good thing ticket cost five dollar would mad paid 50 see crap nobodi identifi charact cardboard cutout stereotyp predict revers stereotyp bad film bad write good actor ugli cartoon craft paul haggi peopl handl anyth bold stroke storytel pictur paint crayon crash depress littl noth provok emot teach noth alreadi know racism prejudic bad thing brain attempt shut part primal impuls self preserv left shatter experi watch film took good two hour fulli recov movi join reveng boogeyman zombiez part hellish triniti horror film certainli not mean distinct good way mean terribl way film no redeem featur everyth appal artless camera work endlessli present us ugliest set imagin stori beyond stupid script script kid annoy lead man charisma free utterli without merit level akin tortur even say tortur mayb would reason explan atroc not pleasant voyag self discoveri highli unrecommend premis sound stupid ye bad noth recommend movi suffer write need suspens mani close up movi seem drag hero didn realli work freedom movi definit averag rent movi bad bad film way long definit one bad one realli don see anyon could enjoy movi don think ever seen movi half bore self indulg piec junk probabl would better director hadn spent movi showcas art work realli isn noteworthi anoth thing didn realli like charact got punch face gallon blood would spew forth soon iron mostli find film total wast time watch pander audienc sabotag film henc whole stori lack certain energi plot simpli rumbl like machin desper depend addit new scene usual hitchcock logic flaw mishima extrem uninterest chilli unremark movi author live work chilli abstrus cultur flat reenact don hold attent emot adrift stagi rest sit aw soldier sing song masculin pledg hairsplit puriti admir sword etc bore piec kill momentum movi quicker anyon els schrader made resum full lousi amateurish film howev recent watch whole thing dvd complet struck extrem stupid storylin contain hole inconsist frankli whole lot crap horrid danc mean realist world would never gotten ballet repertori whole thing quit pathet charact develop also lack depth woa talk aw not wast time horribl worst sappiest dialogu could go realli made unwatch direct poor actor even tell talent not pathet line speak director gave no action check director filmographi site see film didn chanc would not even good made tv flick regrett film fail movi lack visual interest drama express feel celebr patriot underlin narr no actress wors use june allison movi unfortun bad movi plain bad bad script bad direct horribl act make one plain bad dialogu suck cinematographi call suck soundtrack suck act suck concert sequenc suck overal movi cheap trash movi not funni consid ridicul came feel angri not sure tri film turn good day feel night disturb memori guess succeed forc like everyth movi lame movi realli pi ed don even get start jerki camerawork saw theater thought go sick summari wittic weren witti plot well said let one go act bad realli bad even billi bob couldn rise script wors camerawork bad rate 10 liter vomit watch film translat movi live first movi year work experi found offens intellig someon strive great poetri deliv muddl often ridicul stori bunch dispar scene pretenti dialogu get worst kind movi review accur defin pretenti crap find movi intellig even master say intellig imagin obvious use tri make sens piti attempt human natur tri make sens thing one thing toler polit incorrect well artist freedom suspens disbelief slavic femal charact much depth charact man underneath malta set dri barren dialogu movi terribl hockey scene terribl defensemen play like year old goali dive shot 10 feet wide net etc haven seen don wast time call movi drama ridicul start watch didn believ charact particular relationship bakeri assist waitress didn work superfici movi gave feel watch play rather film charact stagey storylin lot like stage farc time pyromaniac waylaid assist bore didn care happen next switch glad didn pay see didn laugh even smile wasn anyth one big yawn storm trooper not even bad enough make list wonder terribl movi lame guy said better dialogu pot plant right everyth movi stupid even love bad movi not watch movi shame new charact weren memor found forget felt though go ireland absolut noth whatsoev didn make scare horrifi make sympathet toward charact simpli annoy jerki camera movement also annoy plot pretti much everi cheap horror pathet attempt give charact depth didn realli work rest plot guess suppos clever twist shed light situat stupid see probabl leav shelf total unfunni movi top pathet unrealist throughout whole 90 minut utter tortur probabl look watch 70000 time luci bell much higher crap sink low quit depress movi make sick watch slacker make excus stupid action 90 minut god never get 90 minut back almost song cover girl old fashion not tune annoy thing cover girl way rita hayworth put pedest unfortun cover girl exampl hollywood use exploit women financi gain couldn take serious fan movi suck realli bad even wors ticker movi bad like build use coupl locat mayb poor hummh blew chosen tortur disgust piec blatant american propaganda show made american stupid full hatr cliché admit elsewher almost everyon involv must return school act utterli predict bad script pile garbag round wish could enter neg valu admin set bad wouldn look place thunderbird episod use slow motion needlessli repeat throughout movi well back bad act bad kind word no continu scene thrown no reason whatsoev vehicl look like made corn flake box directori style border stupid oh yeah storylin pathet hate write bad review film especi realli like star film bad don believ one second anyon could proud not filmmak nor director would hide head sand spent whatev amount money time movi short monument wast time energi would not recommend anyon ever see film came free dvd player bought still turn thing halfway embarrass howel 10 no set 10 act even profession drago debbi rochon terribl direct wors perhap contributori former dialog chimp like camera work bare toler ye simplifi thing sake breviti realli core problem film much go without real fulfil explan convolut plot didn convinc much film watch weird question glanc let girlfriend talk see bad idea hate bela lugosi total extran inton odd line act decid wooden though no wors period univers movi wow bad film not frighten least bare comprehens plot doesn hang togeth act absolut appal not even good camp valu wasn expect oscar materi iq particularli stupid mollusk not see one come film use word loos insult movi go public someon involv knew string togeth narr get 10 simpli noth lower one not watch recent fox movi channel disappoint know contract player littl control script act bad script victor mclaglen even bad brian donlevi almost unrecogniz consid relat screen taylor stanwyck surpris littl chemistri screen two premis film ridicul presid death row scene entir unmov thing wors taylor act stanwyck sing whatev produc go miss entir cheap cheerless heist movi poor characteris lot underbit style stoic emot think chow yun fat better tomorrow cheesi cliché thrown abandon factori readi poorli execut fli judo roll la john woo even squib look aw no point proceed look remot like america plot deriv predict end like merci kill aw dialogu hopeless overact everyon get shot top real wast space time uneasi bad movi not scare dialogu compos thing make littl sens 30 minut footag wast show mediocr elderli actor awkwardli babbl overwrought pseudo satan gibberish corni enough make teen goth blush almost alway old english sometim latin may may not made word direct sloppi best hackney write certainli made even wors bad direct cours act blah act whole cast could put scale balanc perfectli overact underact final get end would great handl compet peopl not jerri falwel particularli rememb sens assault strident cord music would blare littl warn even less mean sort pap screen afternoon punish unemploy not job ten minut film start second thought half way film start dislik time film end not dislik despis film lack convinc script script look rough draft written shoot began finish script complet thing happen charact person chang plot twist occur no real reason script call probabl irrit show ever seen entir life indescrib annoy idiot show ever seen everyth bad could not understand kind idiot would produc mess first place not mention sever season script bad bad contain cheesi uneth joke normal see rate nc 17 movi cast also horribl caus see realli realli bad actor period final word show real tortur zillion time away realiti watch wash machin twirl around wouldn hurt eye much show rate 10 grade note show bad even mother cast pull daughter show 20th centuri fox road hous 1948 not quit silli noir implaus unmitig bore movi full unconvinc cardboard charact blandli written edward chodorov also produc surprisingli direct jean negulesco one would expect great deal widmark charact turn unintent comic lose marbl earli proceed total implaus unconvinc isn enough mess movi pictur also mar constant use studio set indoor exterior whatev prompt documentari beyond seriou spoiler movi huge disappoint mani problem dont know start movi make lot seriou mistak first enough racial stereotyp racial joke offend everyon movi litter overt racial slur toward black cast member return white depict moron boob put race card asid let look major flaw film destroy latifa charact no real plot wouldnt see movi free bipolar ruthless thug one minut ruthless killer next minut luv diaper commerci complet unconvinc terribl disappoint film would receiv mani award accolad especi far deserv work film don think redeem qualiti remak may bad film ever made bad well bad movi solid exampl bad plot bad idea way shame see good actor like thomerson jame make live mess like thought bad film terribl credit 1986 version watchabl let start problem act especi lead professor bad script big flaw mess best exampl dumb write establish turn zombi student back human remov necklac contain piec meteorit director paul matthew also wrote direct weak 1995 monster movi grim clearli doesn know pace film movi terribl bore place light aw film look cheap bland one disappoint aspect lack notabl gore convent never work well past certainli doesn work visual effect aw cg open sequenc space look like could creat microsoft slideshow god sake explos ga tank end aw sum film breeder terribl cheapli made horror movi avoid like ebola viru not recommend almost unbear watch screen littl no charisma terribl comed time asid terribl lead film load debit understand suppos cheap popcorn comedi doesn mean complet insult intellig write incred hackney border offens lewi black consider talent wast incendiari unrestrain pg 13 rate certainli won allow origin fresh first movi spent remak littl els offer budget evid limit reflect not cast also par script borrow much earlier classic howev no decent act action locat work balanc thing filmi less expans fail convey broad sweep landscap great part origin gener lack imagin not recommend possibl movi get wors no plot keep go first place even women final show no sign improv expect thing happen time film might far asleep bewar not trashi cult movi trash period believ even sequel precis give bad review everi element stori top excess phoni contriv pain sit line seem written fifteen year old though tri oh hard sound like fifteen year old would realli um know well talk simpli no excus someth poorli done saw movi thought stupid movi even stupid thought idea volcano lo angel honest unbeliev nonsens foolish conclus not bother movi volcano lo angel noth nonsens stori line aw pain accent absolut abysm also enough hypocrisi film make vomit don wast time watch rubbish non research film omit watch rate 10 basic premis wast sinc sidelin inexplic focu documentari crew regardless film fail level avoid cost know jim connor energet nobodi could much georg well dull realli didn seem want host voic over monoton didn get involv guest movi mind bendingli aw couldn even creat film lack real scare tension medic terminolog use throughout bit iffi say least say insulin depend diabet least said act better even allow poor product valu time 1971 format kind mini seri baaaaaad unless visual collect extant film austen work skip one not good speak music unbear predict kitchi film drop ball mani continu error one user comment differ car garag joe glass one got fact joe facial hair configur seem chang scene scene see movi inspir anyon come overcom fear reject bad actual worth see reason well keep think bad mediocr miser hollow laughabl predict piec garbag case bad laughabl bad perform play angela bennett comput expert home time film noth pretext show ridicul action scene aw still bad actress repeat robot face move pictur result well shame delet film mind one worst show time show would begin smart ass ed comment would total wall uncal fat comput geek unbeliev bibl thumper bad ass girl actor never heard except cole total unbeliev part everi time open mouth expect hear see kid pull plug merci kill horribl show stori unbeliev actor lame would best way describ im big fan rpg game movi disgrac self respect rpger line cut audio everyth wrong find better movi youtub top line don wast time money one bad come lassi movi put sleep forev describ pain dreari time waster film mediocr everi aspect becom dull uninterest mess one forgett movi seen isn even achiev bad good bad memor movi empti hollow shell movi serious not worth wast kid time avoid avoid avoid drive bark mad noth new done second movi movi almost no action scene littl comedi plot hole pair fishnet stock direct edit astonishingli ham fist earth iron film footbal scene end perplex scene strong unpleas bore tire cliché lazi write come thick fast film made film ultra cheap look result film don look right none engag excit plot nonsens doesn interest slightest way uniqu foreign not worth one second time piec trash ever releas beyond act stori charact supposedli special effect etc wrong fact stinker smell like direct video releas avoid cost star trek final frontier worst seri act involv includ like shatner nimoy bad wash make seem old look real life special effect tacki like spock rescu kirk jet pack fall mountain attempt humor piti stori aw dosen bear think basic involv vulcan steal enterpris find god serious didn care film oh not mention uhura belli danc distract male guard place good film garbag worst one seri disappoint wonder could oscar shortlist slow lot hole plot noth becam emperor noth spend 20 year childhood matur age don wast time end day one worst big budget action movi ever seen sure doesn know make coher action movi screenwrit air forc one oblig write script big sum money one worst film ever seen still tri get bad hour 54 minut sheer tedium melodrama horribl act mess script sink feel good lord think lot hole script like bad two hour tv movi imagin everi singl one decis made wrong dialogu atroci act beyond abysm everyth stink troubl write direct make imposs establish thing make movi watchabl like charact stori theme wors incred weak sub plot thrown follow littl band latter day mansonit go report work stori anniversari kill dumb pointless complet wast time short don bother movi also stori act weak around pm bought around 8pm start watch around 15pm fast forward remain film see anyth left watchabl human brain wasn either way suck script horrend stupid stori start fast absolut no suspens build slightest everyth captain howdi say either laughabl plain stupid hell kind crap plot hole could drive semi truck hole dee snider plain suck act one least scari villain ever seen write write damn movi sleep bore throughout whole damn thing act suck music suck script suck pace suck special fx suck direct suck basic movi suck film tri seriou sophist thriller horror flick fail miser probabl one least effect utterli unorigin film ever seen entir life piec cinemat garbag captur celluloid avoid cost rate film stink not funni fulci stay giallo supernatur zombi movi avoid film cost don know happen season five mess consist thread hold seri togeth amaz perform leni parker anita laselva two taelon quiet idealog conflict movi realli dislik one bore horror movi 90 mainli start slow center bore atmospher puppet look realli cheesi not good way like puppet master 80 flick stori lame not interest never realli explain sinist origin puppet aren death scene like previou movi terribl felt asleep first time watch recommend insomniac otherwis don even wast time one fail creat real suspens killer don expect anyth origin even remot frighten sorri realli recommend anyon one bore pointless movi ever seen secondari plot line incomprehens relat primari plot line mystifi hate one worst sandra bullock movi sinc speed not quit bad don understand garbag got shelv movi store not even real movi highli doubt anyon could ever like trash not movi make act like watch wooden puppet move around read book bad warn not rent movi dumbest thing never seen agre jessica movi pretti bad charact one dimension even good guy especi bad guy stori line total predict seen soap opera intellig movi bad charact bad stori bad act realli aw not easi watch whatev avoid groov antithesi good human traffic bad everyon els involv didn share crow level dedic qualiti far better film hand sub par mess movi seem littl slow first act film glad gonna drift away earth far possibl one want surf small wave space movi 1998 deep impact armageddon one fail everywher haven choke vomit end cheap drama worthless dialogu must bore death wast time instead got bore fest whini spoil brat babysit never walk movi faster got bore watch jessic lang take cloth unfortun virtu film product work lost regrett script word embarrass except bad insult one intellig huge wast money crust not good not tasti textur nasti get angri want damn pho honeslti didn tast fresh potato like rubber could tell made ahead time kept warmer would not go back cashier no care ever say still end wayyy overpr disgust pretti sure human hair shock no sign indic cash waitress littl slow servic place not worth time let alon vega not like burritto blah never brought salad ask took hour get food tabl restaur food luke warm sever run around like total overwhelm worst salmon sashimi like final blow seem like good quick place grab bite familiar pub food favor look elsewher poor servic waiter made feel like stupid everi time came tabl servic suck not deal good enough would drag establish hard judg whether side good gross melt styrofoam didn want eat fear get sick frozen puck disgust worst peopl behind regist bad food damn gener horribl attitud toward custom talk one custom don enjoy food heart attack grill downtown vega absolut flat line excus restaur not much seafood like string pasta bottom rip banana not rip petrifi tasteless least think refil water struggl wave 10 minut got food appar never heard salt batter fish chewi servic meh took 30 min get milkshak noth chocol milk guess known place would suck insid excalibur didn use common sens scallop dish quit appal valu well time bad custom servic much good food vega feel cheat wast eat opportun go rice compani come like experienc underwhelm relationship parti wait person ask break walk place smell like old greas trap other eat turkey roast beef bland pan cake everyon rave tast like sugari disast tailor palat six year old poor batter meat ratio made chicken tender unsatisfi summari larg disappoint dine experi never hard rock casino never ever step forward say bye bye tip ladi never go not good real sushi lover let honest yama not good least 40min pass us order food arriv wasn busi pretti gross better atmospher kind hard mess steak although much like look sound place actual experi bit disappoint don know place manag serv blandest food ever eaten prepar indian cuisin worst servic boot least worri host staff lack better word bitch bland not like place number reason don want wast time bad review leav wouldn return place way overpr mediocr food manag rude extrem rude realli mani restaur would love dine weekend vega don waiter wasn help friendli rare check us husband ate lunch disappoint food servic red curri much bamboo shoot wasn tasti menu alway chang food qualiti go servic extrem slow servic littl slow consid serv peopl server food come slow pace give thumb watch waiter pay lot attent tabl ignor us wait forti five minut vain crostini came salad stale unfortun must hit bakeri leftov day everyth order stale came back today sinc reloc still not impress avoid cost don go look good food never treat bad also order spinach avocado salad ingredi sad dress liter zero tast perhap caught night judg review not inspir go back servic leav lot desir liter sat 20 minut no one ask take order burger absolut no flavor meat total bland burger overcook no charcoal flavor also decid not send back waitress look like verg heart attack dress treat rude probabl dirt food suck expect suck could imagin order arriv one gyro miss not realli sure joey vote best hot dog valley reader phoenix magazin live music friday total blow never insult felt disrespect warm beer didn help live sinc 1979 first last time step foot place worst experi ever must night place bug never show would given sure side wall bug climb kitchen 10 minut wait salad realiz wasn come time soon won go back soggi not good small not worth price shower area outsid rins not take full shower unless don mind nude everyon see servic bit lack lobster bisqu bussel sprout risotto filet need salt pepper cours none tabl hope bode go busi someon cook come either cold not enough flavor bad main also uninspir food isn bad enough enjoy deal world worst annoy drunk peopl order doubl cheeseburg got singl patti fall apart pictur upload yeah still suck possibl give zero star descript said yum yum sauc anoth said eel sauc yet anoth said spici mayo well none roll sauc not roll eye may stay not sure go back tri horribl don wast time money time side restaur almost empti no excus wasn busi either also build freez cold like review said couldn pay eat place drink took close 30 minut come one point base sub par servic receiv no effort show gratitud busi won go back noth privileg work eat overal don think would take parent place made similar complaint silent felt well would given godfath zero star possibl tough short flavor bar vega not ever recal charg tap water servic not par either self proclaim coffe cafe wildli disappoint better not dedic boba tea spot even jenni pho think not mac salad pretti bland not get servic stink wait wait place not qualiti sushi not qualiti restaur thing went wrong burn saganaki wait hour breakfast could done 100 time better home hate disagre fellow yelper husband disappoint place wait hour never got either pizza mani around us came later don know slow live neighborhood disappoint won back conveni locat go gave star rate pleas know third time eat bachi burger write review never dine place pleas stay away shrimp stir fri noodl potato chip order sad could probabl count mani chip box probabl around 12 food realli bore greedi corpor never see anoth dime never ever go back much like go back get pass atroci servic never return order toast english muffin came untoast never go back bu boy hand rude point friend basic figur place joke didn mind make publicli loudli known downsid servic also fri without doubt worst fri ever go far mani place never seen restaur serv egg breakfast especi 00 mom got home immedi got sick bite salad server not pleasant deal don alway honor pizza hut coupon everyth gross first bathroom locat dirti seat cover not replenish plain yucki burger got gold standard 17 burger kind disappoint noth authent place spaghetti noth special whatsoev select probabl worst seen vega none sat anoth ten minut final gave left terribl shouldn take 30 min pancak egg sadli gordon ramsey steak place shall sharpli avoid next trip vega buffet small food offer bland definit turn doubt back unless someon els buy find wast food despic wasn food wife lobster bisqu soup lukewarm left stomach ach felt sick rest day drop ball bean rice mediocr best took back money got outta rude inconsider manag staff not friendli wait time serv horribl no one even say hi first 10 minut won back food terribl never ever go back told mani peopl happen don recommend unless car break front starv place deserv one star 90 food disgrac probabl one peopl ever go ian not like one simpli disappoint overal disappoint qualiti food bouchon don account know get screw left frustrat total wast time servic give no star take littl bad servic food suck gave tri eat crust teeth still sore complet gross ground right next tabl larg smear step track everywher pile green bird poop furthermor even find hour oper websit tri like place 10 time think done mistak waiter jerk strike want rush never come order appet took 40 minut pizza anoth 10 minut huge awkward 5lb piec cow 4th gristl fat take busi dinner dollar elsewher noth special not back sauc tasteless don think go back anytim soon far sushi connoisseur definit tell differ good food bad food certainli bad food insult last time lunch bad chicken wing contain driest chicken meat ever eaten definit not worth paid manag worst don think run back carli anytim soon food due fact took 20 minut acknowledg anoth 35 minut get food kept forget thing one note ventil could use upgrad don wast time total letdown would much rather go camelback flower shop cartel coffe third chees friend burger cold ask bill leav without eat didn bring either seafood limit boil shrimp crab leg crab leg definit not tast fresh select food not best dont think back long time attach ga station rare good sign wors humili worker right front bunch horribl name call tragedi struck rather eat airlin food serious would not recommend place overal wasn impress noca gyro basic lettuc terribl servic thoroughli disappoint drive get anyway not think go back greasi unhealthi meal first time might last similarli deliveri man not say word apolog food 45 minut late way expens terribl manag zero star would give zero star worst martini ever wait thirti minut seat although vacant tabl folk wait won tri go back even empti no go eat potato found stranger hair not even hello right dessert bit strang guess mayb went night disgrac howev recent experi particular locat not good know not like restaur someth avoid establish think restaur suffer not tri hard enough salad bland vinegrett babi green heart palm two felt disgust never go back place never ever recommend place anyon server went back forth sever time not even much help hour serious consid theft eew locat need complet overhaul recent wit poor qualiti manag toward guest well wait wait wait pizza tast old super chewi not good way swung give tri deepli disappoint boy sucker dri rate look authent thai food go els pull car wait anoth 15 minut acknowledg hate thing much cheap qualiti black oliv kid play area nasti extrem crumbi pretti tasteless pale color instead nice char no flavor got home see driest damn wing ever not good money got sit fairli fast end wait 40 minut place order anoth 30 minut food arriv want leav not eat owner realli realli need quit soooooo cheap let wrap freak sandwich two paper not one check place coupl year ago not impress chicken got definit reheat ok wedg cold soggi sorri not get food anytim soon friend not like bloodi mari despit hard rate busi actual rare give star not return chicken pho tast bland disappoint drive thru mean not want wait around half hour food somehow end go make us wait wait best luck rude non custom servic focus new manag grandmoth make roast chicken better one ask multipl time wine list time ignor went hostess got one even drastic sick order burger rare came done tri go lunch madhous wait hour seat not greatest mood not eat mayb weren cold would somewhat edibl place lot promis fail deliv bad experi mistak food averag best won go back anytim soon disappoint order 35 big bay plater not much flavor poorli construct fri rice dri well googl mediocr imagin smashburg pop dont go sushi lover avoid place mean plantain worst ever tast servic slow not attent staff spend time talk total brunch fail probabl won back honest went lunch servic slow much say place walk expect amaz quickli disappoint mortifi needless say never back anyway food definit not fill price pay expect chip came drip greas mostli not edibl wasn realli impress strip steak work hospit industri paradis valley refrain recommend cibo longer would not recommend other mean realli get famou fish chip terribl not thing read pleas don go tonight elk filet special suck order old classic new dish go time sore disappoint everyth fli appl juic fli food bare lukewarm must sit wait server bring us ryan bar definit one edinburgh establish won revisit probabl never come back wouldn recommend friend pasta also bad bare touch way fri not sure long stood long enough begin feel awkwardli place open sandwich impress not good way not back watch price inflat portion get smaller manag attitud grow rapidli main thing didn enjoy crowd older crowd around mid 30 wait 30 minut get drink longer get arepa servic poor that nice place not clean food oh stale chicken dish ok beef like shoe leather servic beyond bad tast like dirt sashimi poor qualiti soggi tasteless food not tasti not say real tradit hunan style bother slow servic frozen margarita way sugari tast nutshel restaraunt smell like combin dirti fish market sewer girlfriend veal bad unfortun not good bland flavorless good way describ bare tepid meat chain no fan beat place easili not come back food arriv meh pay 85 hot dog fri look like came kid meal wienerschnitzel not idea good meal brother law work mall ate day guess sick night mediocr food super pissd sad littl veget overcook high hope place sinc burger cook charcoal grill unfortun tast fell flat way flat not singl employe came see ok even need water refil final serv us food server neglig need made us feel unwelcom would not suggest place servic terribl though place overpr not consist boba realli overpr pack food terribl kept get wors wors offici done place honestli blown definit would not eat not wast money aw servic won ever go food qualiti horribl price think place would much rather gone servic fair best love sushi found kabuki price hip servic favor stay away dish poor servic no one tabl thought food averag worth wait paid bill not tip felt server terribl job never bland food surpris consid articl read focus much spice flavor food way overpr portion fuck small 40 buck head realli expect better food won back could not believ dirti oyster place deserv no star would not recommend place disbelief dish qualifi worst version food ever tast bad day not low toler rude custom servic peopl job nice polit wash dish otherwis probabl would not go wife hate meal coconut shrimp friend realli not enjoy meal either fella got huevo ranchero didn look appeal probabl won come back worst food servic hot dish not hot cold dish close room temp watch staff prepar food bare hand no glove everyth deep fri oil feel dine room colleg cook cours high class dine servic slow best start review two star edit give one worst sushi ever eat besid costco weird vibe owner hardli meat better bagel groceri store burger aren good pizza use amaz doughi flavorless found six inch long piec wire salsa servic terribl food mediocr order albondiga soup warm tast like tomato soup frozen meatbal three differ occas ask well done medium well three time got bloodiest piec meat plate two bite refus eat anymor servic extrem slow 20 minut wait got tabl no allergi warn menu waitress absolut no clue meal not contain peanut price bit concern mellow mushroom worst thai ever highli unprofession rude loyal patron spend money elsewher buffet bellagio far anticip drink weak peopl order not correct also feel like chip bought not made hous disappoint dinner went elsewher dessert won return serious cannot believ owner mani unexperienc employe run around like chicken head cut sad felt insult disrespect could talk judg anoth human like call steakhous properli cook steak don understand not impress concept food thing wasn crazi guacamol don like puré realli noth postino hope experi better got food poison buffet brought fresh batch fri think yay someth warm no hilari yummi christma eve dinner rememb biggest fail entir trip us needless say won go back anytim soon place disgust ri style calamari joke howev much garlic fondu bare edibl could bare stomach meal didn complain busi lunch bad lost heart finish also took forev bring us check ask aren one make scene restaur don get definit lost love one disappoint experi food par denni say not good want wait mediocr food downright terribl servic place waaaaaayyyyyyyyyi rate say won go back place fairli clean food simpli wasn worth place lack style sangria half glass wine full 12 ridicul don bother come meat pretti dri slice brisket pull pork build seem pretti neat bathroom pretti trippi wouldn eat equal aw probabl not hurri go back slow seat even reserv not good stretch imagin cashew cream sauc bland veget undercook chipolt ranch dip saus tasteless seem thin water no heat bit sweet not realli spici enough lack flavor disappoint place horribl way overpr mayb vegetarian fare twice thought averag best wasn busi know tabl outsid also dirti lot time worker not alway friendli help menu ambianc not feel like buffet set douchey indoor garden tea biscuit con spotti servic fri not hot neither burger came back cold food came disappoint ensu real disappoint waiter husband said rude not even apolog bad food anyth reason eat would fill night bing drink get carb stomach insult profound deuchebaggeri go outsid smoke break serv solidifi someon order two taco don think may part custom servic ask combo ala cart quit disappoint although blame need place door rave review couldn wait eat disappoint del taco pretti nasti avoid possibl not hard make decent hamburg don like hell no go back gotten much better servic pizza place next door servic receiv restaur don know big deal place won back ya immedi said want talk manag not want talk guy shot firebal behind bar ambianc isn much better unfortun set us disapppoint entre food wasn good server suck wait correct server heimer suck happen next pretti put bad caus know famili own realli want like place overpr get vomit bathroom mid lunch kept look time soon becom 35 minut yet still no food place eat no circumst would ever return top list start tuna sashimi brownish color obvious wasn fresh food averag sure beat nacho movi would expect littl bit come restaur ha long bay bit flop problem charg 11 99 sandwich no bigger subway sub offer better amount veget shrimp unwrap live mile brushfir liter ice cold lack flavor seem undercook dri realli impress place hasn close would avoid place stay mirag refri bean came meal dri crusti food bland spend money time place els ladi tabl next us found live green caterpillar salad present food aw tell disappoint think food flavor textur lack appetit instantli gone overal not impress would not go back whole experi underwhelm think go ninja sushi next time hadn wast enough life pour salt wound draw time took bring check\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 6 - parte 2:\n",
        "\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,12))\n",
        "\n",
        "# Nube de palabras de comentarios positivos:\n",
        "wordcloudnn = WordCloud().generate(pt)\n",
        "ax1.imshow(wordcloudnn, interpolation='bilinear')\n",
        "ax1.axis(\"off\")\n",
        "ax1.set_title('Comentarios Positivos')\n",
        "\n",
        "# Nube de palabras de comentarios negativos:\n",
        "wordcloudnn = WordCloud().generate(nt)\n",
        "ax2.imshow(wordcloudnn, interpolation='bilinear')\n",
        "ax2.axis(\"off\")\n",
        "ax2.set_title('Comentarios Negativos')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################"
      ],
      "metadata": {
        "id": "wiDmIOar9wkS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "1a1ec371-fce2-46c3-d0ed-c931d2411cbe"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "######################################################################\n",
        "###### **AGREGA AQUÍ TUS COMENTARIOS - Pregunta 6 - parte 3:**\n",
        "\n",
        "\n",
        "Primero que nada hay que destacar que tanto los comentarios negativos como los positivos tienen cierto grado de palabras que no aparentan tener coherencia en la forma en que se expresan a la hora de emitir un comentario positivo o negativo, tal es el caso de stori y new year, pues en sí estas palabras no tienen una connotación positiva o negativa.\n",
        "\n",
        "También veo que hay palabras cómo **good** que se repiten (compartiendo tokens) tanto en los comentarios positivos como en los negativos, lo cuál no me parece razonable pues al ser una palabra con notación plenamente positiva, no favorece al análisis a la hora de aparecer en la nube de comentarios negativos, esto puede deberse al sarcasmo, que previamente ya he tocado este tema en el punto de limpieza extra, habría que determinar otra forma de discriminar y encontrar el sentido con otro tipo de métricas la segmentación de este tipo de comentarios, por el momento es de esperarse que no todo sea tan coherente.\n",
        "\n",
        "###### **FIN PARA AGREGAR TUS COMENTARIOS.**\n",
        "######################################################################"
      ],
      "metadata": {
        "id": "2WvHCKq9AGzi"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoD1rSYcDlFH"
      },
      "source": [
        "###**Train-Validation-Test**\n",
        "\n",
        "Para este ejercicio realizaremos una partición de train-validation-test del 70%-15%-15%, respectivamente.\n",
        "\n",
        "Obviamente la distribución de esta partición puede ser otro de los hiperparámetros a determinar, pero para fines de este ejercicio consideraremos dichos valores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xsYb82cDqPN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1ae4572-9504-405f-f3e0-46237de0ae8f"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_val_and_test, y_train, y_val_and_test = train_test_split(Xclean, Y, train_size=.70, shuffle=True, random_state=1)\n",
        "x_val, x_test, y_val, y_test = train_test_split(x_val_and_test, y_val_and_test, test_size=.50, shuffle=True, random_state=17)\n",
        "\n",
        "print('X,y Train:', len(x_train), len(y_train))      # los \"x_\" son \"list\" y los \"y_\" son \"Series\"\n",
        "print('X,y Val:', len(x_val), len(y_val))\n",
        "print('X,y Test', len(x_test), len(y_test))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X,y Train: 2100 2100\n",
            "X,y Val: 450 450\n",
            "X,y Test 450 450\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJ4zQIwhIClF"
      },
      "source": [
        "###Construyamos a continuación nuestro vocabulario de palabras con base al conteo de tokens/palabras del conjunto de entrenamiento."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter"
      ],
      "metadata": {
        "id": "7pNEUJPHDt5U"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvXZKduYIKZ1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23aa6325-7dc8-4fb5-a64c-9ea56c1c6a1f"
      },
      "source": [
        "midiccionario = Counter()\n",
        "\n",
        "for k in range(len(x_train)):\n",
        "  midiccionario.update(x_train[k])\n",
        "\n",
        "\n",
        "print('Longitud del diccionario:', len(midiccionario))\n",
        "print('\\n(word,frequency):')\n",
        "print(midiccionario.most_common(10))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Longitud del diccionario: 3224\n",
            "\n",
            "(word,frequency):\n",
            "[('not', 218), ('good', 163), ('movi', 140), ('great', 139), ('phone', 134), ('film', 130), ('work', 113), ('time', 102), ('like', 101), ('one', 100)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nK-JoAH0PbAm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "85aeed24-366f-456c-a272-ceb4b622d8e6"
      },
      "source": [
        "# Veamos la gráfica de palabras nuestro diccionario con base a la frecuencia de las palabras/tokens:\n",
        "\n",
        "plt.plot(list(np.arange(len(midiccionario))), list(midiccionario.values()), color='blue')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zh6yVo3QJAL"
      },
      "source": [
        "###Descartemos palabras cuya frecuencia en el conjunto de entrenamiento es muy pequeña:"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 7:**"
      ],
      "metadata": {
        "id": "m1GlIF47I15k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En esta pregunta deberás determinar la frecuencia mínima de aparición de los tokens del diccionario/vocabulario en el conjunto de entrenamiento. Es usual que al menos se filtren los tokens de frecuencia 1, ya que usualmente no dan mayor información.\n",
        "\n",
        "Sin embargo, la respuesta a si debemos filtrar todavía tokens de mayor frecuencia dependerá del tamaño que tengas actualmente de tu vocabulario y de la calidad de dichos tokens. Una manera de determinar dicha calidad de los tokens es con el resultado de los modelos de aprendizaje (regresión logística, random-forest, etc.) que aplicarás en los ejercicios de la última parte de esta Actividad.\n",
        "\n",
        "Así que por el momento puedes poner un valor arbitrario y más adelante ajustarlo con base al desempeño que obtengas de los modelos.\n",
        "\n",
        "Sin embargo, debes cuidar que la frecuencia de los tokens no la consideres demasiado grande, que empieces a generar una gran cantidad de comentarios vacíos en tu conjunto de entrenamiento.\n",
        "\n",
        "Llamaremos \"min_freq\" a la frecuencia mínima de aparición de un token para considerarlo dentro del vocabulario/diccionario. Debe ser un número entero.\n",
        "\n",
        "Llamaremos \"midicc\" al nuevo diccionario que generes con tokens de frecuencia natural (es decir, de conteo) mayor o igual a \"min_freq\"."
      ],
      "metadata": {
        "id": "Xm6Du9arI8W8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 7:\n",
        "midicc = {}   # construyamos un diccionario de la forma {key:value} --> {word:freq}\n",
        "min_freq = 2\n",
        "\n",
        "for x in Xclean:     # Tomamos cada comentario ya filtrado o procesado.\n",
        "  for w in x:        # Y ahora cada token de un comentario.\n",
        "\n",
        "    if w in midicc:    # Verificamos si cada llave w ya la incluímos al diccionario.\n",
        "      midicc[w] += 1   # Si ya está incluída, aumentamos en 1 su frecuencia de uso.\n",
        "    else:\n",
        "      midicc[w] = 1    # De lo contrario, si es la primera vez que aparece, la contabilizamos con 1.\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################\n",
        "\n",
        "print('Nueva longitud del nuevo vocabulario:', len(midicc))\n",
        "print(list(midicc.items())[0:5])     # veamos algunos elementos del diccionario."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUwE_U04sbIw",
        "outputId": "cd616c86-35af-4d07-e715-7ef70a3be004"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nueva longitud del nuevo vocabulario: 3959\n",
            "[('no', 83), ('way', 48), ('plug', 16), ('us', 25), ('unless', 7)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDA2xvl_StVP"
      },
      "source": [
        "###Filtremos los conjuntos Train, Validation y Test con respecto al nuevo vocabulario propuesto:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1np99AiTQQT"
      },
      "source": [
        "train_x = []\n",
        "for ss in x_train:\n",
        "  train_x.append([w for w in ss if w in midicc])\n",
        "\n",
        "val_x = []\n",
        "for ss in x_val:\n",
        "  val_x.append([w for w in ss if w in midicc])\n",
        "\n",
        "test_x = []\n",
        "for ss in x_test:\n",
        "  test_x.append([w for w in ss if w in midicc])"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTfkWb_DUAsE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c958e7f0-def6-4014-dec6-72b62f7ac566"
      },
      "source": [
        "# Podemos ver algunos de los comentarios de entrenamiento, antes y después\n",
        "# de incluir la condición de la frecuencia mínima de ocurrencia de un token:\n",
        "\n",
        "for k in range(3):\n",
        "  print('Antes:', x_train[k])\n",
        "  print('Después:', train_x[k])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Antes: ['co', 'star', 'don', 'fare', 'much', 'better', 'peopl', 'like', 'morgan', 'freeman', 'jonah', 'hill', 'ed', 'helm', 'wast']\n",
            "Después: ['co', 'star', 'don', 'fare', 'much', 'better', 'peopl', 'like', 'morgan', 'freeman', 'jonah', 'hill', 'ed', 'helm', 'wast']\n",
            "Antes: ['tonight', 'elk', 'filet', 'special', 'suck']\n",
            "Después: ['tonight', 'elk', 'filet', 'special', 'suck']\n",
            "Antes: ['paid', 'bill', 'not', 'tip', 'felt', 'server', 'terribl', 'job']\n",
            "Después: ['paid', 'bill', 'not', 'tip', 'felt', 'server', 'terribl', 'job']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Obtengamos las matrices de conteo document-term-matrix como matrices dispersas (sparse matrices ). Para ello requerimos que cada comentario esté como un solo string."
      ],
      "metadata": {
        "id": "lwnhTff1Pgh8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_x_docs = []\n",
        "for k in range(len(train_x)):\n",
        "  train_x_docs.append(' '.join(train_x[k]))\n",
        "\n",
        "val_x_docs = []\n",
        "for k in range(len(val_x)):\n",
        "  val_x_docs.append(' '.join(val_x[k]))\n",
        "\n",
        "test_x_docs = []\n",
        "for k in range(len(test_x)):\n",
        "  test_x_docs.append(' '.join(test_x[k]))\n",
        "\n",
        "\n",
        "\n",
        "# Verifica que los primeros comentarios de Train están cada uno como un solo string:\n",
        "for k in range(3):\n",
        "  print(train_x_docs[k])"
      ],
      "metadata": {
        "id": "XTeYtxD6QTzO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea56423e-7663-49b5-d8d7-037e844272c8"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "co star don fare much better peopl like morgan freeman jonah hill ed helm wast\n",
            "tonight elk filet special suck\n",
            "paid bill not tip felt server terribl job\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Ahora generemos las matrices sparse en su formato predeterminado CSR de estas matrices de conteo:"
      ],
      "metadata": {
        "id": "XvZYTeJFR2Ue"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mivocab = list(midicc.keys())     # Obtenemos nuestro vocabulario de tokens/palabras.\n",
        "\n",
        "#mivocab.sort()    # Usualmente se ordenan alfabéticamente, pero para visualizar algunos de los valores\n",
        "                   # no cero de las matrices, por el momento no lo haremos.\n",
        "\n",
        "\n",
        "\n",
        "# Generamos las matrices dispersas requeridas con base al conteo de los tokens:\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "countvectorizer = CountVectorizer(vocabulary=mivocab)\n",
        "train_x_count = countvectorizer.fit_transform(train_x_docs)\n",
        "val_x_count = countvectorizer.transform(val_x_docs)\n",
        "test_x_count = countvectorizer.transform(test_x_docs)\n",
        "\n",
        "count_tokens = countvectorizer.get_feature_names_out()\n",
        "df_countvect = pd.DataFrame(data = train_x_count.toarray(),  columns = count_tokens)\n",
        "print(df_countvect.iloc[0:3,6:18])   # veamos las frecuencias de los primeros comentarios."
      ],
      "metadata": {
        "id": "E5FGslE6PYlM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c59a5de3-e460-400e-f674-f46d7b677961"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   convert  good  case  excel  valu  great  jawbon  tie  charger  convers  \\\n",
            "0        0     0     0      0     0      0       0    0        0        0   \n",
            "1        0     0     0      0     0      0       0    0        0        0   \n",
            "2        0     0     0      0     0      0       0    0        0        0   \n",
            "\n",
            "   last  45  \n",
            "0     0   0  \n",
            "1     0   0  \n",
            "2     0   0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p_sparse = 1 - train_x_count.count_nonzero() / (train_x_count.shape[0] * train_x_count.shape[1])    # Sparsity : Porcentaje de valores de 0.\n",
        "print('Porcentaje de valores cero de la matriz sparse de entrenamiento: %.1f%%' % (100*p_sparse))"
      ],
      "metadata": {
        "id": "ADFOT_HuVyRT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1563d53-c948-4f6f-ed30-bcb9c2029fbc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Porcentaje de valores cero de la matriz sparse de entrenamiento: 99.8%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 8:**"
      ],
      "metadata": {
        "id": "L-S6wtXrXyJI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este ejercicio deberás ahora generar las matrices dispersas TF-IDF para los conjuntos de entrenamiento, validación y prueba.\n",
        "\n",
        "Deberás usar el vocabulario \"mivocab\" para generar dichas matrices.\n",
        "\n",
        "Dichas matrices las llamaremos \"train_x_tfidf\", \"val_x_tfidf\" y \"test_x_tfidf\".\n",
        "\n",
        "Deberás agregar además las librerías o módulos necesarios.\n",
        "\n",
        "Al final imprime los valores de algunos tokens de los primeros tres comentarios."
      ],
      "metadata": {
        "id": "f8MQL5_HYBp4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 8:\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tfidfvectorizer = TfidfVectorizer(vocabulary = mivocab, norm=None)\n",
        "train_x_tfidf = tfidfvectorizer.fit_transform(train_x_docs)\n",
        "val_x_tfidf = tfidfvectorizer.transform(val_x_docs)\n",
        "test_x_tfidf = tfidfvectorizer.transform(test_x_docs)\n",
        "\n",
        "tfidf_tokens = tfidfvectorizer.get_feature_names_out()\n",
        "df_tfidfvect = pd.DataFrame(data = test_x_tfidf.toarray(),  columns = tfidf_tokens)\n",
        "print(df_tfidfvect.iloc[0:3,0:])\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################"
      ],
      "metadata": {
        "id": "2XnZSfRiYqqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f7b3143-6d03-4f58-95cb-600de14f94dd"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         no  way  plug   us  unless        go  convert  good  case  excel  \\\n",
            "0  0.000000  0.0   0.0  0.0     0.0  0.000000      0.0   0.0   0.0    0.0   \n",
            "1  4.572631  0.0   0.0  0.0     0.0  4.401673      0.0   0.0   0.0    0.0   \n",
            "2  0.000000  0.0   0.0  0.0     0.0  0.000000      0.0   0.0   0.0    0.0   \n",
            "\n",
            "   ...  brushfir  mirag  refri  crusti  caterpillar  appetit  instantli  \\\n",
            "0  ...       0.0    0.0    0.0     0.0          0.0      0.0        0.0   \n",
            "1  ...       0.0    0.0    0.0     0.0          0.0      0.0        0.0   \n",
            "2  ...       0.0    0.0    0.0     0.0          0.0      0.0        0.0   \n",
            "\n",
            "   ninja  pour  wound  \n",
            "0    0.0   0.0    0.0  \n",
            "1    0.0   0.0    0.0  \n",
            "2    0.0   0.0    0.0  \n",
            "\n",
            "[3 rows x 3959 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 9:**"
      ],
      "metadata": {
        "id": "bPl1-LILa2ya"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###A continuación, usando Sklearn, deberás aplicar los modelos de regresión logística (lo llamaremos modeloLRcount), ranfom-forest para clasificación (lo llamaremos modeloRFcount) y naive-bayes en su opción MultinomialNB (lo llamaremos modeloNBcount).\n",
        "\n",
        "###Deberás de utilizar las matrices de conteo \"train_x_count\", \"val_x_count\" y \"test_x_count\".\n",
        "\n",
        "###Con el mejor modelo obtenido, se usará el conjunto de prueba (test) para obtener el desempeño (Accuracy) final y su matriz de confusión.\n",
        "\n",
        "###Simplemente deberás buscar los mejores hiperparámetros de cada modelo.\n",
        "\n",
        "###No es necesario usar por el momento validación-cruzada (cross-validation).\n",
        "\n",
        "###Puedes probar primero cada uno de manera independiente para la búsqueda de los mejores hiperparámetros y ya que los tengas, incluir aquí tus mejores modelos de cada uno."
      ],
      "metadata": {
        "id": "RvJ1-GhNa8NP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB"
      ],
      "metadata": {
        "id": "6Plkf1hBX1D7"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 9:\n",
        "\n",
        "\n",
        "modeloLRcount = LogisticRegression()\n",
        "modeloLRcount.fit(train_x_count,y_train)\n",
        "\n",
        "\n",
        "modeloRFcount = RandomForestClassifier()\n",
        "modeloRFcount.fit(train_x_count,y_train)\n",
        "\n",
        "\n",
        "modeloNBcount = MultinomialNB()\n",
        "modeloNBcount.fit(train_x_count,y_train)\n",
        "\n",
        "\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################\n",
        "\n",
        "\n",
        "print('LR: Train-accuracy: %.2f%%' % (100*modeloLRcount.score(train_x_count, y_train)))\n",
        "print('LR: Val-accuracy: %2.f%%' % (100*modeloLRcount.score(val_x_count, y_val)))\n",
        "\n",
        "print('\\nRF: Train-accuracy: %.2f%%' % (100*modeloRFcount.score(train_x_count, y_train)))\n",
        "print('RF: Val-accuracy: %.2f%%' % (100*modeloRFcount.score(val_x_count, y_val)))\n",
        "\n",
        "print('\\nNB: Train-accuracy: %.2f%%' % (100*modeloNBcount.score(train_x_count, y_train)))\n",
        "print('NB: Val-accuracy: %.2f%%' % (100*modeloNBcount.score(val_x_count, y_val)))"
      ],
      "metadata": {
        "id": "YQ7FyoOvjcXh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "035c8df1-6813-4f60-e12c-ead6e1fd52e8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: Train-accuracy: 97.10%\n",
            "LR: Val-accuracy: 86%\n",
            "\n",
            "RF: Train-accuracy: 100.00%\n",
            "RF: Val-accuracy: 84.44%\n",
            "\n",
            "NB: Train-accuracy: 94.62%\n",
            "NB: Val-accuracy: 83.56%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ EL NOMBRE DE TU MEJOR MODELO OBTENIDO CON COUNTER:\n",
        "\n",
        "mejor_modelo_Count = modeloRFcount # incluye el nombre, modeloXXcount, de tu mejor modelo.\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################\n",
        "\n",
        "\n",
        "\n",
        "print('Test-accuracy con el mejor modelo de Conteo %.2f%%' % (100*mejor_modelo_Count.score(test_x_count, y_test)))\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "pred = mejor_modelo_Count.predict(test_x_count)\n",
        "print('\\nMatriz de confusión con el mejor modelo de Conteo:')\n",
        "print(confusion_matrix(y_test, pred, labels=[0,1]))\n",
        "\n",
        "print('\\nMatriz de confusión con el mejor modelo de Conteo en proporciones:')\n",
        "print(confusion_matrix(y_test, pred, labels=[0,1]) / pred.shape[0])"
      ],
      "metadata": {
        "id": "RGlYDiAof3Wq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47536b29-e579-45dc-e66e-5b402044e089"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test-accuracy con el mejor modelo de Conteo 80.22%\n",
            "\n",
            "Matriz de confusión con el mejor modelo de Conteo:\n",
            "[[180  36]\n",
            " [ 53 181]]\n",
            "\n",
            "Matriz de confusión con el mejor modelo de Conteo en proporciones:\n",
            "[[0.4        0.08      ]\n",
            " [0.11777778 0.40222222]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 10:**"
      ],
      "metadata": {
        "id": "dfMzR65Hhs-h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Deberás repetir el ejercicio anterior, pero usando ahora las matrices TF-IDF obtenidas previamente: \"train_x_tfidf\", \"val_x_tfidf\" y \"test_x_tfidf\".\n",
        "\n",
        "###Llamaremos a los modelos modeloXXtfidf, para LR, RF y NB."
      ],
      "metadata": {
        "id": "OIU6j92_iTDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ TUS LÍNEAS DE CÓDIGO - Pregunta 10:\n",
        "\n",
        "\n",
        "modeloLRtfidf = LogisticRegression()\n",
        "modeloLRtfidf.fit(train_x_tfidf, y_train)\n",
        "\n",
        "\n",
        "modeloRFtfidf = RandomForestClassifier()\n",
        "modeloRFtfidf.fit(train_x_tfidf, y_train)\n",
        "\n",
        "\n",
        "\n",
        "modeloNBtfidf = MultinomialNB()\n",
        "modeloNBtfidf.fit(train_x_tfidf, y_train)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################\n",
        "\n",
        "\n",
        "print('Resultados parciales con matrices tf-idf:')\n",
        "print('\\nLR: Train-accuracy: %.2f%%' % (100*modeloLRtfidf.score(train_x_tfidf, y_train)))\n",
        "print('LR: Val-accuracy: %2.f%%' % (100*modeloLRtfidf.score(val_x_tfidf, y_val)))\n",
        "\n",
        "print('\\nRF: Train-accuracy: %.2f%%' % (100*modeloRFtfidf.score(train_x_tfidf, y_train)))\n",
        "print('RF: Val-accuracy: %.2f%%' % (100*modeloRFtfidf.score(val_x_tfidf, y_val)))\n",
        "\n",
        "print('\\nNB: Train-accuracy: %.2f%%' % (100*modeloNBtfidf.score(train_x_tfidf, y_train)))\n",
        "print('NB: Val-accuracy: %.2f%%' % (100*modeloNBtfidf.score(val_x_tfidf, y_val)))"
      ],
      "metadata": {
        "id": "QZ_IF9jxdjkv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0049fe86-ab8a-43b2-f4e9-80cfc3fcafd6"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultados parciales con matrices tf-idf:\n",
            "\n",
            "LR: Train-accuracy: 99.71%\n",
            "LR: Val-accuracy: 83%\n",
            "\n",
            "RF: Train-accuracy: 100.00%\n",
            "RF: Val-accuracy: 84.44%\n",
            "\n",
            "NB: Train-accuracy: 95.90%\n",
            "NB: Val-accuracy: 78.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##############################################################################\n",
        "# AGREGA AQUÍ EL NOMBRE DE TU MEJOR MODELO OBTENIDO CON TF-IDF:\n",
        "\n",
        "mejor_modelo_Tfidf = modeloRFtfidf  # incluye el nombre, modeloXXtfidf, de tu mejor modelo.\n",
        "\n",
        "# FIN PARA AGREGAR TUS LÍNEAS DE CÓDIGO.\n",
        "##############################################################################\n",
        "\n",
        "\n",
        "\n",
        "print('Test-accuracy con el mejor modelo Tf-idf %.2f%%' % (100*mejor_modelo_Tfidf.score(test_x_tfidf, y_test)))\n",
        "\n",
        "pred = mejor_modelo_Tfidf.predict(test_x_tfidf)\n",
        "print('\\nMatriz de confusión con el mejor modelo Tf-idf:')\n",
        "print(confusion_matrix(y_test, pred, labels=[0,1]))\n",
        "\n",
        "print('\\nMatriz de confusión con el mejor modelo de Tf-idf en proporciones:')\n",
        "print(confusion_matrix(y_test, pred, labels=[0,1]) / pred.shape[0])"
      ],
      "metadata": {
        "id": "Fk-cvsF3djd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6570b79-9292-465d-e7c8-89f7d77d43f8"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test-accuracy con el mejor modelo Tf-idf 80.67%\n",
            "\n",
            "Matriz de confusión con el mejor modelo Tf-idf:\n",
            "[[182  34]\n",
            " [ 53 181]]\n",
            "\n",
            "Matriz de confusión con el mejor modelo de Tf-idf en proporciones:\n",
            "[[0.40444444 0.07555556]\n",
            " [0.11777778 0.40222222]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Pregunta - 11:**"
      ],
      "metadata": {
        "id": "T2i2LXPBqVRS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Finalmente incluye tus conclusiones finales de la Actividad.\n",
        "\n",
        "###En particular, indica cuál técnica resultó mejor en este problema, ¿usando matrices de conteo o tf-idf?\n",
        "\n",
        "###Indica también a qué crees que se debe la diferencia entre los valores de los Falsos-Negativos y Falsos-Positivos de la mejor matriz de confusión que obtuviste. ¿Qué implicaciones podrían tener cada uno de estos errores?\n",
        "\n",
        "##**Como nota complementaria, se esperaría que tu mejor modelo final mínimamente debiera alcanzar un 72% de buen desempeño (accuracy), pero puede ser mayor.**"
      ],
      "metadata": {
        "id": "QNHZDkH7qXDy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "######################################################################\n",
        "###### **AGREGA AQUÍ TUS CONCLUSIONES FINALES - Pregunta 11:**\n",
        "\n",
        "\n",
        "Esta actividad ha sido de gran aporte y aprendizaje, sobre todo para aplicar los conocimientos que hemos estado aterrizando desde la semana tres, con los temas básicos de diccionarios, stemming, y lemmatization (técnicas básicas) cuya función es bajar el coste computacional de las palabras que tienen una raíz conjugada o simplemente pueden ser mayormente simplificadas para su procesamiento más adelante para optimizar el cómputo.\n",
        "\n",
        "Uno de los detalles más importante que me queda cómo ligera inquietud, es cómo definir el tema del sarcasmo en una reseña o el doble sentido, una persona puede comprender perfectamente por medio de la entonación o el rating que alguien está haciendo una crítica negativa a una película o serie. Por ejemplo si yo en la evaluación pongo ($*$) 1/5 y escribo algo cómo: \"la mejor película que he visto en mi vida\", claramente estoy haciendo una crítica negativa pues estoy aludiendo al sarcasmo y la burla. Pero este tema es de vital importancia diferenciarlo a la hora de hacer limpieza, pues en la gráfica de nube puede suceder lo que he comentado reiteradas veces: **good** no necesariamente significa que la película haya sido buena aunque se compartan los tokens entre cadenas, y debido a esto se debe de buscar o implementar una discriminación metaheurística que ayude a clasificar de mejor manera este tipo de situaciones.\n",
        "\n",
        "Por otro lado, tf-idf es una técnica que sirve para evaluar la importancia de los términos en datos, mientras que las matrices dispersas (sparse) son una forma de eficientar la memoria al representar conjuntos de datos grandes y dispersos. Entonces, recapitulando: las matrices dispersas se usan para almacenar representaciones tf-idf de datos de texto y así eficientan la capacidad de comprimir formatos de elementos diferentes a cero. Algo que destacar es la significa diferencia entre la eficiencia previo y después al método sparse pues se obtuvo 80.22% y luego 80.67%. Esto se puede reflejar en la matriz de confusión final:\n",
        "\n",
        "$\\begin{bmatrix}\n",
        "180 & 36 \\\\\n",
        "53 & 181\n",
        "\\end{bmatrix}$ vs $\\begin{bmatrix}\n",
        "182 & 34 \\\\\n",
        "53 & 181\n",
        "\\end{bmatrix}$ y partiendo de $\\begin{bmatrix}\n",
        "T_p & F_p \\\\\n",
        "F_n & T_n\n",
        "\\end{bmatrix}$ con el ajuste de la matriz dispersiva podemos recuperar una mejor clasificación al predicir 2 datos más cómo verdaderos positivos.\n",
        "\n",
        "\n",
        "###### **FIN PARA AGREGAR TUS CONCLUSIONES FINALES.**\n",
        "######################################################################"
      ],
      "metadata": {
        "id": "WRF0z0gorGh1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**FIN DE LA ACTIVIDAD DE LAS SEMANAS 3 Y 4**"
      ],
      "metadata": {
        "id": "IDvRXhA_rdBy"
      }
    }
  ]
}