Text_Preprocess_and_Vectorzie.ipynb

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      "name": "Text Preprocess and Vectorzie",
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        "<a href=\"https://colab.research.google.com/github/hosseiniii/Comments-with-Sentiment-Label/blob/main/Text_Preprocess_and_Vectorzie.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/dirve')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqHm7P6QsJdp",
        "outputId": "a9b05058-7a17-4291-ecf9-1e7589053ca9"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/dirve\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/dirve/MyDrive/BFP"
      ],
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        },
        "id": "Yxa1WrMazfUA",
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/dirve/MyDrive/BFP\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install required packages"
      ],
      "metadata": {
        "id": "l3Sba12drZxK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q hazm\n",
        "!pip install -q clean-text[gpl]\n",
        "!pip install -q fasttext"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofHm23CxrUgp",
        "outputId": "0917c66e-60ec-4a36-b583-364e42dc365b"
      },
      "execution_count": 34,
      "outputs": [
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          "text": [
            "\u001b[?25l\r\u001b[K     |████▊                           | 10 kB 18.4 MB/s eta 0:00:01\r\u001b[K     |█████████▌                      | 20 kB 21.9 MB/s eta 0:00:01\r\u001b[K     |██████████████▎                 | 30 kB 24.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 40 kB 25.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 51 kB 12.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 61 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 68 kB 4.3 MB/s \n",
            "\u001b[?25h  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the required libraries"
      ],
      "metadata": {
        "id": "4k34_9_Hnxdj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "I7soiYfJniyV"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import fasttext\n",
        "\n",
        "# Text Cleaning\n",
        "import string\n",
        "import re\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from cleantext import clean\n",
        "import hazm\n",
        "\n",
        "# Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objects as go"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Datasets"
      ],
      "metadata": {
        "id": "ZRFZ3TRyqT2H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_comments_df = pd.read_excel(\"comment.xlsx\")\n",
        "unlabeled_comments_df.drop([\"product_id\", \"confirmed_at\"], axis=1, inplace=True)\n",
        "unlabeled_comments_df = unlabeled_comments_df.rename(columns={'comment': 'text'})\n",
        "unlabeled_comments_df.head()"
      ],
      "metadata": {
        "id": "PeFM8Q5sqDAS",
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          "height": 303
        },
        "outputId": "41045d51-fbe1-4e62-de33-803f17bb104e"
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      "execution_count": 5,
      "outputs": [
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              "                                                text\n",
              "0  سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...\n",
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              "2  متاسفانه عمر مفید این ماشین کم هست و بعد از دو...\n",
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              "4  این تیغ محصول کشور آمریکا هست ( در عکس محصول م..."
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    {
      "cell_type": "code",
      "source": [
        "digi1_dataset = pd.read_csv(\"dataset-digi-1ta3.csv\")\n",
        "digi2_dataset = pd.read_csv(\"digi2.csv\")\n",
        "sentipers_dataset = pd.read_csv(\"sentipers.csv\")\n",
        "\n",
        "\n",
        "digi1_dataset.drop(\"Score\", axis=1, inplace=True)\n",
        "digi2_dataset.drop(\"Unnamed: 0\", axis=1, inplace=True)\n",
        "sentipers_dataset.drop([\"Unnamed: 0\", \"index\", \"sid\", \"file\"], axis=1, inplace=True)\n",
        "\n",
        "digi1_dataset = digi1_dataset.rename(columns={'Text': 'text', 'Suggestion': 'value'})\n",
        "sentipers_dataset = sentipers_dataset.rename(columns={'polarity': 'value'})\n",
        "\n",
        "\n",
        "labeled_comments_df = pd.DataFrame(data=digi1_dataset)\n",
        "labeled_comments_df = labeled_comments_df.append(digi2_dataset, ignore_index=True)\n",
        "labeled_comments_df = labeled_comments_df.append(sentipers_dataset, ignore_index=True)\n",
        "\n",
        "labeled_comments_df.head()"
      ],
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          "height": 220
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        "id": "-9v7I0VY1Lbw",
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              "                                                text  value\n",
              "0  این اولین تجربه من برای خرید ایفون هست   امروز...      1\n",
              "1                     خرید این محصول رو توصیه میکنم       1\n",
              "2  1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...      1\n",
              "3  سلام خدمت دوستان این گوشی از همه نظر عالی  کیف...      1\n",
              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...      1"
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      "cell_type": "code",
      "source": [
        "print(\"1. Positive \\n2. neutral \\n3. Negative\\n\")\n",
        "labeled_comments_df['value'].value_counts()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. Positive \n",
            "2. neutral \n",
            "3. Negative\n",
            "\n"
          ]
        },
        {
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              "1    10841\n",
              "2     6607\n",
              "3     2536\n",
              "Name: value, dtype: int64"
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    {
      "cell_type": "markdown",
      "source": [
        "## Text Preprocessing"
      ],
      "metadata": {
        "id": "urt8aSWtadgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_comments_df['comment_len_by_words'] = labeled_comments_df['text'].apply(lambda t: len(hazm.word_tokenize(t)))\n",
        "labeled_comments_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "HdVbNka3ow73",
        "outputId": "f87a0abe-a73e-4443-d432-3f795cc6d0e4"
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      "execution_count": 8,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                                text  value  \\\n",
              "0  این اولین تجربه من برای خرید ایفون هست   امروز...      1   \n",
              "1                     خرید این محصول رو توصیه میکنم       1   \n",
              "2  1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...      1   \n",
              "3  سلام خدمت دوستان این گوشی از همه نظر عالی  کیف...      1   \n",
              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...      1   \n",
              "\n",
              "   comment_len_by_words  \n",
              "0                    24  \n",
              "1                     6  \n",
              "2                    11  \n",
              "3                    29  \n",
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      "cell_type": "code",
      "source": [
        "unlabeled_comments_df['comment_len_by_words'] = unlabeled_comments_df['text'].apply(lambda t: len(hazm.word_tokenize(str(t))))\n",
        "unlabeled_comments_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "i85shUikwkpb",
        "outputId": "fef8223a-4c20-4d38-dc24-fcaa6631a5ee"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  comment_len_by_words\n",
              "0  سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...                   221\n",
              "1  بوی تند ولی خوشبو داره.ماندگاریش خوبه و هر چقد...                    16\n",
              "2  متاسفانه عمر مفید این ماشین کم هست و بعد از دو...                    43\n",
              "3  افا از وقتی من اینو واسه پسرم خریدم همه بچه ها...                    81\n",
              "4  این تیغ محصول کشور آمریکا هست ( در عکس محصول م...                    87"
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              "                                                     [key], {});\n",
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              "\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "min_max_len = labeled_comments_df[\"comment_len_by_words\"].min(), labeled_comments_df[\"comment_len_by_words\"].max()\n",
        "print(f'Labeled Min Words: {min_max_len[0]} \\tLabeled Max Words: {min_max_len[1]}')\n",
        "\n",
        "min_max_len = unlabeled_comments_df[\"comment_len_by_words\"].min(), unlabeled_comments_df[\"comment_len_by_words\"].max()\n",
        "print(f'Unlabeled Min Words: {min_max_len[0]} \\tUnlabeled Max Words: {min_max_len[1]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnFksFCgpZlq",
        "outputId": "591ee52c-8799-49a8-af60-5fea9019c563"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Labeled Min Words: 1 \tLabeled Max Words: 1243\n",
            "Unlabeled Min Words: 1 \tUnlabeled Max Words: 2170\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def data_gl_than(data, less_than=100.0, greater_than=0.0, col='comment_len_by_words'):\n",
        "    data_length = data[col].values\n",
        "\n",
        "    data_glt = sum([1 for length in data_length if greater_than < length <= less_than])\n",
        "\n",
        "    data_glt_rate = (data_glt / len(data_length)) * 100\n",
        "\n",
        "    print(f'Texts with word length of greater than {greater_than} and less than {less_than} includes {data_glt_rate:.2f}% of the whole!')"
      ],
      "metadata": {
        "id": "0r_bqP9lpg3Z"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_gl_than(labeled_comments_df, 256, 3)\n",
        "data_gl_than(unlabeled_comments_df, 256, 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MErp5ZIqpo24",
        "outputId": "e95bf535-5099-44aa-bca4-bb95d83cdded"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texts with word length of greater than 3 and less than 256 includes 96.99% of the whole!\n",
            "Texts with word length of greater than 3 and less than 256 includes 95.33% of the whole!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "minlim, maxlim = 3, 256"
      ],
      "metadata": {
        "id": "zB4PrkPlpvuv"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# remove comments with the length of fewer than three words\n",
        "labeled_comments_df['comment_len_by_words'] = labeled_comments_df['comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else None)\n",
        "labeled_comments_df = labeled_comments_df.dropna(subset=['comment_len_by_words'])\n",
        "labeled_comments_df = labeled_comments_df.reset_index(drop=True)\n",
        "\n",
        "unlabeled_comments_df['comment_len_by_words'] = unlabeled_comments_df['comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else None)\n",
        "unlabeled_comments_df = unlabeled_comments_df.dropna(subset=['comment_len_by_words'])\n",
        "unlabeled_comments_df = unlabeled_comments_df.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "0dwLaLphqlFO"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Histogram(\n",
        "    x=labeled_comments_df['comment_len_by_words'],\n",
        "    name=\"Labeled Comments\" \n",
        "))\n",
        "\n",
        "fig.add_trace(go.Histogram(\n",
        "    x=unlabeled_comments_df['comment_len_by_words'],\n",
        "    name=\"Unlabeled Comments\" \n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text='Distribution of word counts within comments',\n",
        "    xaxis_title_text='Word Count',\n",
        "    yaxis_title_text='Frequency',\n",
        "    bargap=0.2,\n",
        "    bargroupgap=0.2)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "5_jaZ_DZqzFv",
        "outputId": "18134535-174d-40b4-ba74-9207b3b8b3d5"
      },
      "execution_count": 20,
      "outputs": [
        {
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          "data": {
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    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "groupby_rate = labeled_comments_df.groupby('value')['value'].count()\n",
        "\n",
        "fig.add_trace(go.Bar(\n",
        "    x=list(sorted(groupby_rate.index)),\n",
        "    y=groupby_rate.tolist(),\n",
        "    text=groupby_rate.tolist(),\n",
        "    textposition='auto'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text='Distribution of Comment Scores within comments',\n",
        "    xaxis_title_text='Score',\n",
        "    yaxis_title_text='Frequency',\n",
        "    bargap=0.2,\n",
        "    bargroupgap=0.2)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ytAl9CFAr05O",
        "outputId": "764238dc-f971-4833-ea3b-a0ab93a9fdf1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "def rate_to_label(rate):\n",
        "    if rate == 1:\n",
        "        return 'positive'\n",
        "    elif rate == 2:\n",
        "      return 'neutral'\n",
        "    else:\n",
        "        return 'negative'\n",
        "\n",
        "\n",
        "labeled_comments_df['label'] = labeled_comments_df['value'].apply(lambda t: rate_to_label(t))\n",
        "labels = list(sorted(labeled_comments_df['label'].unique()))\n",
        "labeled_comments_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "lK53SrUryTeb",
        "outputId": "ea92f9ef-44b3-4fed-c957-02190220b2c5"
      },
      "execution_count": 23,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                                text  value  \\\n",
              "0  این اولین تجربه من برای خرید ایفون هست   امروز...      1   \n",
              "1                     خرید این محصول رو توصیه میکنم       1   \n",
              "2  1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...      1   \n",
              "3  سلام خدمت دوستان این گوشی از همه نظر عالی  کیف...      1   \n",
              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...      1   \n",
              "\n",
              "   comment_len_by_words     label  \n",
              "0                  24.0  positive  \n",
              "1                   6.0  positive  \n",
              "2                  11.0  positive  \n",
              "3                  29.0  positive  \n",
              "4                  55.0  positive  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a37ffce2-3631-428e-a336-dc4a48bb596b\">\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>value</th>\n",
              "      <th>comment_len_by_words</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>این اولین تجربه من برای خرید ایفون هست   امروز...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>خرید این محصول رو توصیه میکنم</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>سلام خدمت دوستان این گوشی از همه نظر عالی  کیف...</td>\n",
              "      <td>1</td>\n",
              "      <td>29.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...</td>\n",
              "      <td>1</td>\n",
              "      <td>55.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a37ffce2-3631-428e-a336-dc4a48bb596b 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-a37ffce2-3631-428e-a336-dc4a48bb596b');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   fixing unicodes\n",
        "*   removing specials like a phone number, email, url, new lines, ...\n",
        "*   cleaning HTMLs\n",
        "*   normalizing\n",
        "*   removing emojis"
      ],
      "metadata": {
        "id": "ZtQ1-cUM0AjM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cleanhtml(raw_html):\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    cleantext = re.sub(cleanr, '', raw_html)\n",
        "    return cleantext\n",
        "\n",
        "\n",
        "def cleaning(text):\n",
        "    text = text.strip()\n",
        "    \n",
        "    # regular cleaning\n",
        "    text = clean(text,\n",
        "        fix_unicode=True,\n",
        "        to_ascii=False,\n",
        "        lower=True,\n",
        "        no_line_breaks=True,\n",
        "        no_urls=True,\n",
        "        no_emails=True,\n",
        "        no_phone_numbers=True,\n",
        "        no_numbers=False,\n",
        "        no_digits=False,\n",
        "        no_currency_symbols=True,\n",
        "        no_punct=False,\n",
        "        replace_with_url=\"\",\n",
        "        replace_with_email=\"\",\n",
        "        replace_with_phone_number=\"\",\n",
        "        replace_with_number=\"\",\n",
        "        replace_with_digit=\"0\",\n",
        "        replace_with_currency_symbol=\"\",\n",
        "    )\n",
        "\n",
        "    # cleaning htmls\n",
        "    text = cleanhtml(text)\n",
        "    \n",
        "    # normalizing\n",
        "    normalizer = hazm.Normalizer()\n",
        "    text = normalizer.normalize(text)\n",
        "    \n",
        "    # removing wierd patterns\n",
        "    wierd_pattern = re.compile(\"[\"\n",
        "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "        u\"\\U00002702-\\U000027B0\"\n",
        "        u\"\\U000024C2-\\U0001F251\"\n",
        "        u\"\\U0001f926-\\U0001f937\"\n",
        "        u'\\U00010000-\\U0010ffff'\n",
        "        u\"\\u200d\"\n",
        "        u\"\\u2640-\\u2642\"\n",
        "        u\"\\u2600-\\u2B55\"\n",
        "        u\"\\u23cf\"\n",
        "        u\"\\u23e9\"\n",
        "        u\"\\u231a\"\n",
        "        u\"\\u3030\"\n",
        "        u\"\\ufe0f\"\n",
        "        u\"\\u2069\"\n",
        "        u\"\\u2066\"\n",
        "        # u\"\\u200c\"\n",
        "        u\"\\u2068\"\n",
        "        u\"\\u2067\"\n",
        "        \"]+\", flags=re.UNICODE)\n",
        "    \n",
        "    text = wierd_pattern.sub(r'', text)\n",
        "    \n",
        "    # removing extra spaces, hashtags\n",
        "    text = re.sub(\"#\", \"\", text)\n",
        "    text = re.sub(\"\\s+\", \" \", text)\n",
        "    \n",
        "    return text"
      ],
      "metadata": {
        "id": "Go7cS7xV1Esu"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# cleaning labeled comments\n",
        "labeled_comments_df['cleaned_comment'] = labeled_comments_df['text'].apply(cleaning)\n",
        "\n",
        "\n",
        "# calculate the length of comments based on their words\n",
        "labeled_comments_df['cleaned_comment_len_by_words'] = labeled_comments_df['cleaned_comment'].apply(lambda t: len(hazm.word_tokenize(t)))\n",
        "\n",
        "# remove comments with the length of fewer than three words\n",
        "labeled_comments_df['cleaned_comment_len_by_words'] = labeled_comments_df['cleaned_comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else len_t)\n",
        "data = labeled_comments_df.dropna(subset=['cleaned_comment_len_by_words'])\n",
        "data = data.reset_index(drop=True)\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XGnUAeCL1X4u",
        "outputId": "6f551e13-e669-4a8a-e0eb-5101f74f3aed"
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      "execution_count": 28,
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              "                                                text  value  \\\n",
              "0  این اولین تجربه من برای خرید ایفون هست   امروز...      1   \n",
              "1                     خرید این محصول رو توصیه میکنم       1   \n",
              "2  1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...      1   \n",
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              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...      1   \n",
              "\n",
              "   comment_len_by_words     label  \\\n",
              "0                  24.0  positive   \n",
              "1                   6.0  positive   \n",
              "2                  11.0  positive   \n",
              "3                  29.0  positive   \n",
              "4                  55.0  positive   \n",
              "\n",
              "                                     cleaned_comment  \\\n",
              "0  این اولین تجربه من برای خرید ایفون هست امروز ب...   \n",
              "1                      خرید این محصول رو توصیه میکنم   \n",
              "2    ۱ ساله این گوشی رو دارم هیچ نقطه ضعفی ازش ندیدم   \n",
              "3  سلام خدمت دوستان این گوشی از همه نظر عالی کیفی...   \n",
              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...   \n",
              "\n",
              "   cleaned_comment_len_by_words  \n",
              "0                            24  \n",
              "1                             6  \n",
              "2                            11  \n",
              "3                            29  \n",
              "4                            55  "
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              "      <td>positive</td>\n",
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              "      <td>6</td>\n",
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              "      <td>1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "labeled_comments_df = labeled_comments_df[['cleaned_comment', 'label']]\n",
        "labeled_comments_df.columns = ['comment', 'label']\n",
        "labeled_comments_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "omc0dwRQ3EuS",
        "outputId": "1be88569-648c-47aa-cdba-5f18ffe5af3e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             comment     label\n",
              "0  این اولین تجربه من برای خرید ایفون هست امروز ب...  positive\n",
              "1                      خرید این محصول رو توصیه میکنم  positive\n",
              "2    ۱ ساله این گوشی رو دارم هیچ نقطه ضعفی ازش ندیدم  positive\n",
              "3  سلام خدمت دوستان این گوشی از همه نظر عالی کیفی...  positive\n",
              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...  positive"
            ],
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              "      <th></th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "# cleaning unlabeled comments\n",
        "unlabeled_comments_df['cleaned_comment'] = unlabeled_comments_df['text'].apply(cleaning)\n",
        "\n",
        "\n",
        "# calculate the length of comments based on their words\n",
        "unlabeled_comments_df['cleaned_comment_len_by_words'] = unlabeled_comments_df['cleaned_comment'].apply(lambda t: len(hazm.word_tokenize(t)))\n",
        "\n",
        "# remove comments with the length of fewer than three words\n",
        "unlabeled_comments_df['cleaned_comment_len_by_words'] = unlabeled_comments_df['cleaned_comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else len_t)\n",
        "data = unlabeled_comments_df.dropna(subset=['cleaned_comment_len_by_words'])\n",
        "data = data.reset_index(drop=True)\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "M8iW6enc3Wam",
        "outputId": "fbe5a841-43e9-4ece-94ea-0c9754be7bac"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  comment_len_by_words  \\\n",
              "0  سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...                 221.0   \n",
              "1  بوی تند ولی خوشبو داره.ماندگاریش خوبه و هر چقد...                  16.0   \n",
              "2  متاسفانه عمر مفید این ماشین کم هست و بعد از دو...                  43.0   \n",
              "3  افا از وقتی من اینو واسه پسرم خریدم همه بچه ها...                  81.0   \n",
              "4  این تیغ محصول کشور آمریکا هست ( در عکس محصول م...                  87.0   \n",
              "\n",
              "                                     cleaned_comment  \\\n",
              "0  سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...   \n",
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              "4  این تیغ محصول کشور آمریکا هست (در عکس محصول مش...   \n",
              "\n",
              "   cleaned_comment_len_by_words  \n",
              "0                           220  \n",
              "1                            16  \n",
              "2                            42  \n",
              "3                            76  \n",
              "4                            87  "
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_comments_df = unlabeled_comments_df[['cleaned_comment']]\n",
        "unlabeled_comments_df.columns = ['comment']\n",
        "unlabeled_comments_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "Y32E0bSq4eqf",
        "outputId": "529a6480-4b88-4b85-8051-828982e6b2b7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             comment\n",
              "0  سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...\n",
              "1  بوی تند ولی خوشبو داره. ماندگاریش خوبه و هر چق...\n",
              "2  متاسفانه عمر مفید این ماشین کم هست و بعد از دو...\n",
              "3  افا از وقتی من اینو واسه پسرم خریدم همه بچه‌ها...\n",
              "4  این تیغ محصول کشور آمریکا هست (در عکس محصول مش..."
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>comment</th>\n",
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              "      <td>سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...</td>\n",
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              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>افا از وقتی من اینو واسه پسرم خریدم همه بچه‌ها...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>این تیغ محصول کشور آمریکا هست (در عکس محصول مش...</td>\n",
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              "\n",
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              "                                                     [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",
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              "  "
            ]
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          "metadata": {},
          "execution_count": 33
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load PreTrain Fasttext model"
      ],
      "metadata": {
        "id": "5hPFUigW7PQr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%time\n",
        "model = fasttext.load_model(\"cc.fa.300.bin\")\n",
        "print(len(model.get_sentence_vector('بوی تند ولی خوشبو داره. ماندگاریش خوبه و هر چقدر میگذره خوشبوتر میشه..')))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHZUn_GQ5FAp",
        "outputId": "0309d27e-e7ef-4762-b523-2eac2a5c197e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
            "Wall time: 7.87 µs\n",
            "300\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "07jtXKlT7NyC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_comment_vector(comment):\n",
        "  return model.get_sentence_vector(comment)"
      ],
      "metadata": {
        "id": "C9KQGRhf7oji"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_comments_df['vector'] = labeled_comments_df['comment'].apply(lambda t: get_comment_vector(t))\n",
        "labeled_comments_df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        },
        "id": "jlk5OTsE6p1q",
        "outputId": "287517d8-d742-44da-a981-40ac0acbc664"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
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              "                                             comment     label  \\\n",
              "0  این اولین تجربه من برای خرید ایفون هست امروز ب...  positive   \n",
              "1                      خرید این محصول رو توصیه میکنم  positive   \n",
              "\n",
              "                                              vector  \n",
              "0  [0.022520525, 0.05368863, -0.020799093, -0.013...  \n",
              "1  [0.04413482, 0.05712284, -0.042128816, -0.0379...  "
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-a909cccb-3b6f-4229-b0ae-5714f1e99f9c 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-a909cccb-3b6f-4229-b0ae-5714f1e99f9c');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_comments_df['vector'] = unlabeled_comments_df['comment'].apply(lambda t: get_comment_vector(t))\n",
        "unlabeled_comments_df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1022
        },
        "id": "Jjo1uTJs8cdz",
        "outputId": "6573675f-d795-4dbc-de8b-64f8b2f62ba5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             comment  \\\n",
              "0  سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...   \n",
              "1  بوی تند ولی خوشبو داره. ماندگاریش خوبه و هر چق...   \n",
              "\n",
              "                                              vector  \n",
              "0  [0.020869812, 0.053931676, -0.012462843, 0.000...  \n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>vector</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>سلام، قبل اینکه نظرم رو بگم میخواستم به یک موض...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بوی تند ولی خوشبو داره. ماندگاریش خوبه و هر چق...</td>\n",
              "      <td>[-0.0105422335, 0.060699597, -0.014465652, -0....</td>\n",
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              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dda9cfe9-54af-4fce-b292-608e6ec90901 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-dda9cfe9-54af-4fce-b292-608e6ec90901');\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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save Dataframes to csv file"
      ],
      "metadata": {
        "id": "PNnDLsrr_Dqt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_comments_df.to_csv(\"labeled_comments.csv\")\n",
        "unlabeled_comments_df.to_csv(\"unlabeled_comments.csv\")"
      ],
      "metadata": {
        "id": "Bx6MqWSK96Pt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}