Sentiment_Analysis_NLTK.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCLXmQeuyUkp",
        "outputId": "c5c44287-3d3e-46c9-d837-ed01b6d4dcb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: watchdog, tenacity, smmap, pydeck, gitdb, gitpython, streamlit\n",
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            "Successfully installed gitdb-4.0.11 gitpython-3.1.43 pydeck-0.9.1 smmap-5.0.1 streamlit-1.37.0 tenacity-8.5.0 watchdog-4.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install streamlit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoCdzgHVqvTj",
        "outputId": "89cb28f4-45fc-4495-f826-5566b8925707"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing sentiment.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile sentiment.py\n",
        "import streamlit as st\n",
        "import nltk\n",
        "from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "nltk.download('vader_lexicon')\n",
        "\n",
        "# Title for the app\n",
        "st.title(\"Sentiment Analysis Model\")\n",
        "st.write(\"Sentiment analysis, also known as opinion mining or emotion artificial intelligence, is a method that uses natural language processing (NLP), text analysis, computational linguistics, and biometrics to identify, extract, quantify, and study affective states and subjective information. It involves analyzing digital text to determine if the emotional tone of the message is positive, negative, or neutral.\")\n",
        "\n",
        "# Text input field\n",
        "user_input = st.text_input(\"Enter text for analyzing the sentiment:\")\n",
        "\n",
        "# Initialize the Sentiment Intensity Analyzer\n",
        "sia = SentimentIntensityAnalyzer()\n",
        "\n",
        "# Get the sentiment score\n",
        "sentiment_score = sia.polarity_scores(user_input)\n",
        "\n",
        "# Determine the sentiment based on the compound score\n",
        "if sentiment_score['compound'] >= 0.1:\n",
        "    sentiment = 'Positive'\n",
        "elif sentiment_score['compound'] <= -0.1:\n",
        "    sentiment = 'Negative'\n",
        "else:\n",
        "    sentiment = 'Neutral'\n",
        "\n",
        "if st.button(\"Analyze Text\"):\n",
        "  # Display the input text\n",
        "  st.write(\"You entered:\", user_input)\n",
        "  st.write(\"The sentiment of the text is:\", sentiment)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wx3rpmMYx634",
        "outputId": "18d2e94a-d8a6-4db9-a49f-b760bcb296ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K\u001b[?25h\n",
            "added 22 packages, and audited 23 packages in 2s\n",
            "\n",
            "3 packages are looking for funding\n",
            "  run `npm fund` for details\n",
            "\n",
            "2 \u001b[33m\u001b[1mmoderate\u001b[22m\u001b[39m severity vulnerabilities\n",
            "\n",
            "To address all issues, run:\n",
            "  npm audit fix\n",
            "\n",
            "Run `npm audit` for details.\n"
          ]
        }
      ],
      "source": [
        "!npm install localtunnel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNX5nek9yAd-",
        "outputId": "830538b4-a86e-4784-cd66-68853ea33518"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n",
            "\u001b[0m\n",
            "your url is: https://three-toys-smoke.loca.lt\n",
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Local URL: \u001b[0m\u001b[1mhttp://localhost:8501\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://34.125.198.44:8501\u001b[0m\n",
            "\u001b[0m\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "!streamlit run /content/sentiment.py & npx localtunnel --port 8501"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hxKv5Urcz2Zi"
      },
      "outputs": [],
      "source": [
        "!pip list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9WDa7PriyJ4W"
      },
      "outputs": [],
      "source": [
        "# Define the content of the requirements.txt file\n",
        "requirements = \"\"\"\n",
        "gitdb==4.0.11\n",
        "gitpython==3.1.43\n",
        "pydeck==0.8.1b0\n",
        "smmap==5.0.1\n",
        "streamlit==1.33.0\n",
        "watchdog==4.0.0\n",
        "nltk==3.8.1\n",
        "\"\"\"\n",
        "\n",
        "# Write the content to the requirements.txt file\n",
        "with open('requirements.txt', 'w') as f:\n",
        "    f.write(requirements)\n",
        "\n",
        "# Verify if the file is created\n",
        "import os\n",
        "if os.path.exists('requirements.txt'):\n",
        "    print(\"requirements.txt file created successfully!\")\n",
        "else:\n",
        "    print(\"Error creating requirements.txt file.\")\n",
        "\n",
        "from google.colab import files\n",
        "# Download the requirements.txt file\n",
        "files.download('requirements.txt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e9P7sJch1dff"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "nltk.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ads0yOFi1e_S"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}