Twitter_Sentiment_Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sergyDwhiz/SentStream/blob/main/Twitter_Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXzZj8V6lcd2",
        "outputId": "c02af5ee-8e8a-4720-9f6d-0455bcb499b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle==1.5.12 in /usr/local/lib/python3.10/dist-packages (1.5.12)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle==1.5.12) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle==1.5.12) (2024.7.4)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle==1.5.12) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle==1.5.12) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle==1.5.12) (4.66.4)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle==1.5.12) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle==1.5.12) (2.0.7)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle==1.5.12) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle==1.5.12) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle==1.5.12) (3.7)\n"
          ]
        }
      ],
      "source": [
        "# Install Kaggle library\n",
        "!pip install kaggle==1.5.12\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eTnEMK75mY4o"
      },
      "outputs": [],
      "source": [
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDaurXy0pNX6"
      },
      "source": [
        "Upload Kaggle.json file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zR_TyQn-oGrv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36ab22eb-feb8-42de-cfd1-3692c0a2cbf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot stat 'kaggle.json': No such file or directory\n",
            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTddkf-bpa0H"
      },
      "source": [
        "Import Twitter Sentiment Dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3f0TytncphuK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9774aa12-49eb-42ef-ec50-8bac676c8138"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/kazanova/sentiment140\n",
            "License(s): other\n",
            "Downloading sentiment140.zip to /content\n",
            " 94% 76.0M/80.9M [00:00<00:00, 103MB/s] \n",
            "100% 80.9M/80.9M [00:00<00:00, 91.5MB/s]\n"
          ]
        }
      ],
      "source": [
        "# API to fetch the dataset from Kaggle\n",
        "! kaggle datasets download -d kazanova/sentiment140"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfMEIQrdr00w"
      },
      "source": [
        "Extract ZIP file (Compressed Dataset)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sYrRMKSPrc_B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9f184f9-34a2-4086-b930-0d2c44aa7f2b"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'sentiment140.zip'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-873440f30b48>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sentiment140.zip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mzip_ref\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mzip_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Extracts to the current directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The Dataset is extracted!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/zipfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, compression, allowZip64, compresslevel, strict_timestamps)\u001b[0m\n\u001b[1;32m   1251\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1252\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1253\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilemode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1254\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1255\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfilemode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodeDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'sentiment140.zip'"
          ]
        }
      ],
      "source": []
    },
    {
      "source": [
        "import zipfile\n",
        "import os\n",
        "\n",
        "# Check if the file exists before attempting to extract\n",
        "if os.path.exists('sentiment140.zip'):\n",
        "    with zipfile.ZipFile('sentiment140.zip', 'r') as zip_ref:\n",
        "        zip_ref.extractall('.')  # Extracts to the current directory\n",
        "        print(\"The Dataset is extracted!\")\n",
        "else:\n",
        "    print(\"Error: 'sentiment140.zip' not found. Make sure you have downloaded the dataset.\")"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fflkRz2_ssGg",
        "outputId": "22440ab7-c7d3-41f0-da07-430c0fb28fa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Dataset is extracted!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ca2TaM4FtKmM"
      },
      "source": [
        "Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q3tWOj86s1fY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35e6af18-3bf9-4b21-90af-22272eaac5cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully imported!\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re # Regular Expression\n",
        "from nltk.corpus import stopwords # Natural Language Toolkit.\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Successfully imported!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iR3MGkSBtg35",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d335966f-fc89-4c01-e114-14020edaa5c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oenx7FUov95D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1a8b61d-a8ac-477a-c5e7-0c4e04cdb843"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['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"
          ]
        }
      ],
      "source": [
        "# Print stopwatch in English.\n",
        "print(stopwords.words('english'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkGBK2mTwmS9"
      },
      "source": [
        "Data Processing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QPOLF0ibwAOQ"
      },
      "outputs": [],
      "source": [
        "# Load pandas data from csv file to pandas dataframe\n",
        "twitter_data = pd.read_csv('training.1600000.processed.noemoticon.csv', encoding='latin-1', header=None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubzNk1zWwixR"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SFRLQGG6xxuB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2ceb9f3-025e-4235-b7eb-928fd5006b35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1600000, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Check the number of rows and columns\n",
        "twitter_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9DNXi-3zx1dx"
      },
      "outputs": [],
      "source": []
    },
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      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "0e883adc-1f4d-47d9-ff18-9b0e3640e903"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0           1                             2         3                4  \\\n",
              "0  0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY  _TheSpecialOne_   \n",
              "1  0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY    scotthamilton   \n",
              "2  0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY         mattycus   \n",
              "3  0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY          ElleCTF   \n",
              "4  0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY           Karoli   \n",
              "\n",
              "                                                   5  \n",
              "0  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
              "1  is upset that he can't update his Facebook by ...  \n",
              "2  @Kenichan I dived many times for the ball. Man...  \n",
              "3    my whole body feels itchy and like its on fire   \n",
              "4  @nationwideclass no, it's not behaving at all....  "
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              "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>scotthamilton</td>\n",
              "      <td>is upset that he can't update his Facebook by ...</td>\n",
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              "      <td>1467810917</td>\n",
              "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>mattycus</td>\n",
              "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>1467811184</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>ElleCTF</td>\n",
              "      <td>my whole body feels itchy and like its on fire</td>\n",
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              "      <td>0</td>\n",
              "      <td>1467811193</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>Karoli</td>\n",
              "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
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              "\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-035f8b48-d1ab-4556-a235-03df25127db7 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-035f8b48-d1ab-4556-a235-03df25127db7');\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",
              "\n",
              "\n",
              "<div id=\"df-f4f00ec5-feb9-4856-8824-6c652faea5ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4f00ec5-feb9-4856-8824-6c652faea5ce')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .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-f4f00ec5-feb9-4856-8824-6c652faea5ce 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": "twitter_data"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Print first five rows of dataframe\n",
        "twitter_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sz_OKHkLyT3C"
      },
      "outputs": [],
      "source": [
        "# Name the colums and read the datasets again\n",
        "twitter_data = pd.read_csv('training.1600000.processed.noemoticon.csv', encoding='latin-1', header=None, names=['target', 'id', 'date', 'flag', 'user', 'text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KkIFGyt-zE-j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5e52293-1830-4e81-f155-2c9233be0bd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1600000, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Check the number of rows and columns\n",
        "twitter_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xV4-hJYsyF8M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "d32553bc-44d0-4415-bf65-1f39062631d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target          id                          date      flag  \\\n",
              "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
              "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
              "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
              "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "\n",
              "              user                                               text  \n",
              "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
              "1    scotthamilton  is upset that he can't update his Facebook by ...  \n",
              "2         mattycus  @Kenichan I dived many times for the ball. Man...  \n",
              "3          ElleCTF    my whole body feels itchy and like its on fire   \n",
              "4           Karoli  @nationwideclass no, it's not behaving at all....  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-90b9e6b4-729e-456e-ac6c-8a654e57873d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>flag</th>\n",
              "      <th>user</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1467810369</td>\n",
              "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>_TheSpecialOne_</td>\n",
              "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1467810672</td>\n",
              "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>scotthamilton</td>\n",
              "      <td>is upset that he can't update his Facebook by ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1467810917</td>\n",
              "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>mattycus</td>\n",
              "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1467811184</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>ElleCTF</td>\n",
              "      <td>my whole body feels itchy and like its on fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1467811193</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>Karoli</td>\n",
              "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90b9e6b4-729e-456e-ac6c-8a654e57873d')\"\n",
              "            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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-90b9e6b4-729e-456e-ac6c-8a654e57873d 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-90b9e6b4-729e-456e-ac6c-8a654e57873d');\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",
              "\n",
              "\n",
              "<div id=\"df-d590a9e5-6cc4-4314-9818-162abac9ebba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d590a9e5-6cc4-4314-9818-162abac9ebba')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .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-d590a9e5-6cc4-4314-9818-162abac9ebba 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": "twitter_data"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Print first five rows of dataframe\n",
        "twitter_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mIwUf-Qgy4Ou",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a17637c2-90f7-45fa-90f0-2caa59be1a9b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target    0\n",
              "id        0\n",
              "date      0\n",
              "flag      0\n",
              "user      0\n",
              "text      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Count the number of missing values in this dataset\n",
        "twitter_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yJ8vOdmpzpnG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0311f29a-c030-43d2-cdbf-f36b84d7dbfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "0    800000\n",
              "4    800000\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Check the distribution of target columns (# Postive and Negatuive values)\n",
        "twitter_data['target'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9JBtR9q0g5M"
      },
      "source": [
        "Convert the target \"4\" to \"1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iONX1c-b1FoW"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZwpVtxAi0ml9"
      },
      "outputs": [],
      "source": [
        "twitter_data.replace({'target':{4:1}}, inplace=True) # We need just positive (1) and negative (0) entires, so no need for neutral..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G0OxqpAT15oa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d35d9deb-c733-43a9-b109-b6622a00966e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "0    800000\n",
              "1    800000\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "twitter_data['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mOttwg_k16HP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XedaMoTQ2Unv"
      },
      "source": [
        "- 0: Negative Tweet\n",
        "- 1: Positive Tweet\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWdRba3A2otM"
      },
      "source": [
        "Stemming\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26e01HHP2z0M"
      },
      "source": [
        "Stemming is the process of reducing a word to it's keyword or rootword."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrP5kzMo2lVT"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QN4jZCRS20l_"
      },
      "outputs": [],
      "source": [
        "port_stem = PorterStemmer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QSmEerUP4Gu5"
      },
      "outputs": [],
      "source": [
        "def stemming(content):\n",
        "    # Initialize PorterStemmer within the function\n",
        "    port_stem = PorterStemmer()\n",
        "\n",
        "    stemmed_content = re.sub('[^a-zA-Z]', ' ', content) # Remove all non-alphabet charaters.\n",
        "    stemmed_content = stemmed_content.lower() # Convert all letters to lowercase\n",
        "    stemmed_content = stemmed_content.split() # Split the string into a list of words\n",
        "    # Now port_stem is accessible here\n",
        "    stemmed_content = [port_stem.stem(word) for word in stemmed_content if not word in stopwords.words('english')] # Stem the words and remove stopwords\n",
        "    stemmed_content = ' '.join(stemmed_content) # Join the words back into a string\n",
        "\n",
        "    return stemmed_content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ffW4F-646a8E"
      },
      "outputs": [],
      "source": [
        "twitter_data['stemmed_content'] = twitter_data['text'].apply(stemming)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "smxF6A6NUsKY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "33d25735-98ea-4f8d-d60d-9d4baf461021"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target          id                          date      flag  \\\n",
              "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
              "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
              "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
              "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "\n",
              "              user                                               text  \\\n",
              "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...   \n",
              "1    scotthamilton  is upset that he can't update his Facebook by ...   \n",
              "2         mattycus  @Kenichan I dived many times for the ball. Man...   \n",
              "3          ElleCTF    my whole body feels itchy and like its on fire    \n",
              "4           Karoli  @nationwideclass no, it's not behaving at all....   \n",
              "\n",
              "                                     stemmed_content  \n",
              "0  switchfoot http twitpic com zl awww bummer sho...  \n",
              "1  upset updat facebook text might cri result sch...  \n",
              "2  kenichan dive mani time ball manag save rest g...  \n",
              "3                    whole bodi feel itchi like fire  \n",
              "4                      nationwideclass behav mad see  "
            ],
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              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
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              "        document.querySelector('#df-c6f073bb-e341-497f-bd7d-8b65005d7ca5 button.colab-df-convert');\n",
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              "                                                    [key], {});\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "twitter_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(twitter_data['stemmed_content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fiE4fK0A9-qS",
        "outputId": "eff9a757-fe30-4403-83fa-3d271af77676"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0          switchfoot http twitpic com zl awww bummer sho...\n",
            "1          upset updat facebook text might cri result sch...\n",
            "2          kenichan dive mani time ball manag save rest g...\n",
            "3                            whole bodi feel itchi like fire\n",
            "4                              nationwideclass behav mad see\n",
            "                                 ...                        \n",
            "1599995                           woke school best feel ever\n",
            "1599996    thewdb com cool hear old walt interview http b...\n",
            "1599997                         readi mojo makeov ask detail\n",
            "1599998    happi th birthday boo alll time tupac amaru sh...\n",
            "1599999    happi charitytuesday thenspcc sparkschar speak...\n",
            "Name: stemmed_content, Length: 1600000, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(twitter_data['target'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QqEP1RZK-Qfg",
        "outputId": "a40354dd-2e0d-4400-e7c1-1eeddac51f9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0          0\n",
            "1          0\n",
            "2          0\n",
            "3          0\n",
            "4          0\n",
            "          ..\n",
            "1599995    1\n",
            "1599996    1\n",
            "1599997    1\n",
            "1599998    1\n",
            "1599999    1\n",
            "Name: target, Length: 1600000, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separating the data and the label\n",
        "X = twitter_data['stemmed_content'].values\n",
        "Y = twitter_data['target'].values"
      ],
      "metadata": {
        "id": "jolC9WpL-nSb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o25olt7M_A6N",
        "outputId": "0146954f-a2ae-4ffd-a299-85760dea46a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['switchfoot http twitpic com zl awww bummer shoulda got david carr third day'\n",
            " 'upset updat facebook text might cri result school today also blah'\n",
            " 'kenichan dive mani time ball manag save rest go bound' ...\n",
            " 'readi mojo makeov ask detail'\n",
            " 'happi th birthday boo alll time tupac amaru shakur'\n",
            " 'happi charitytuesday thenspcc sparkschar speakinguph h']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNhDOseJ_HDM",
        "outputId": "e5503b83-5006-4b1c-a3e1-115d03595686"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data to training data and test data\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "metadata": {
        "id": "p65lL3gr_K5K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwvvMw39_UCG",
        "outputId": "d048aaf2-2c67-4b7c-ba86-8abb7a21c48a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1600000,) (1280000,) (320000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-cH3XeZ_ibk",
        "outputId": "f3121ee5-67bf-43c6-ac45-bf8b27942ba4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['watch saw iv drink lil wine' 'hatermagazin'\n",
            " 'even though favourit drink think vodka coke wipe mind time think im gonna find new drink'\n",
            " ... 'eager monday afternoon'\n",
            " 'hope everyon mother great day wait hear guy store tomorrow'\n",
            " 'love wake folger bad voic deeper']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bm6FLaSC_2fn",
        "outputId": "c401cec1-a108-4776-cbb1-e6ddbbf2efb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 0 ... 1 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82ipDTdI_4Ho",
        "outputId": "43d9f56f-c063-48cf-82a3-7bfe92688bd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['mmangen fine much time chat twitter hubbi back summer amp tend domin free time'\n",
            " 'ah may show w ruth kim amp geoffrey sanhueza'\n",
            " 'ishatara mayb bay area thang dammit' ...\n",
            " 'destini nevertheless hooray member wonder safe trip' 'feel well'\n",
            " 'supersandro thank']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the textual data to numerical data\n",
        "vectorizer = TfidfVectorizer()\n",
        "vectorizer.fit(X_train)\n",
        "\n",
        "X_train = vectorizer.transform(X_train)\n",
        "X_test = vectorizer.transform(X_test)\n"
      ],
      "metadata": {
        "id": "SYNxdo4X_8UG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)\n",
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7mXmWSQAMEL",
        "outputId": "e8a741a4-b936-4b2d-accc-a80ac4c8ee2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 443066)\t0.4484755317023172\n",
            "  (0, 436713)\t0.27259876264838384\n",
            "  (0, 354543)\t0.3588091611460021\n",
            "  (0, 235045)\t0.41996827700291095\n",
            "  (0, 185193)\t0.5277679060576009\n",
            "  (0, 109306)\t0.3753708587402299\n",
            "  (1, 160636)\t1.0\n",
            "  (2, 443430)\t0.3348599670252845\n",
            "  (2, 433560)\t0.3296595898028565\n",
            "  (2, 409143)\t0.15169282335109835\n",
            "  (2, 407301)\t0.18709338684973031\n",
            "  (2, 406399)\t0.32105459490875526\n",
            "  (2, 288470)\t0.16786949597862733\n",
            "  (2, 266729)\t0.24123230668976975\n",
            "  (2, 178061)\t0.1619010109445149\n",
            "  (2, 150715)\t0.18803850583207948\n",
            "  (2, 132311)\t0.2028971570399794\n",
            "  (2, 129411)\t0.29074192727957143\n",
            "  (2, 124484)\t0.1892155960801415\n",
            "  (2, 109306)\t0.4591176413728317\n",
            "  (2, 77929)\t0.31284080750346344\n",
            "  (3, 411528)\t0.27089772444087873\n",
            "  (3, 406399)\t0.29029991238662284\n",
            "  (3, 388626)\t0.3940776331458846\n",
            "  (3, 172421)\t0.37464146922154384\n",
            "  :\t:\n",
            "  (1279996, 373144)\t0.35212500999832036\n",
            "  (1279996, 318303)\t0.21254698865277744\n",
            "  (1279996, 291078)\t0.17981734369155505\n",
            "  (1279996, 238078)\t0.5606696159563151\n",
            "  (1279996, 238077)\t0.5249170684084672\n",
            "  (1279996, 237899)\t0.22365675600992338\n",
            "  (1279997, 273084)\t0.4353549002982409\n",
            "  (1279997, 112591)\t0.7574829183045267\n",
            "  (1279997, 5685)\t0.48650358607431304\n",
            "  (1279998, 435463)\t0.2851807874350361\n",
            "  (1279998, 412553)\t0.2816582375021589\n",
            "  (1279998, 385313)\t0.4103285865588191\n",
            "  (1279998, 275288)\t0.38703346602729577\n",
            "  (1279998, 169461)\t0.2659980990397061\n",
            "  (1279998, 162047)\t0.34691726958159064\n",
            "  (1279998, 156297)\t0.3137096161546449\n",
            "  (1279998, 153281)\t0.28378968751027456\n",
            "  (1279998, 124765)\t0.32241752985927996\n",
            "  (1279998, 93795)\t0.21717768937055476\n",
            "  (1279999, 435572)\t0.31691096877786484\n",
            "  (1279999, 433612)\t0.3607341026233411\n",
            "  (1279999, 242268)\t0.19572649660865402\n",
            "  (1279999, 135384)\t0.6130934129868719\n",
            "  (1279999, 96224)\t0.5416162421321443\n",
            "  (1279999, 31410)\t0.248792678366695\n",
            "  (0, 420984)\t0.17915624523539803\n",
            "  (0, 409143)\t0.31430470598079707\n",
            "  (0, 398906)\t0.3491043873264267\n",
            "  (0, 388348)\t0.21985076072061738\n",
            "  (0, 279082)\t0.1782518010910344\n",
            "  (0, 271016)\t0.4535662391658828\n",
            "  (0, 171378)\t0.2805816206356073\n",
            "  (0, 138164)\t0.23688292264071403\n",
            "  (0, 132364)\t0.25525488955578596\n",
            "  (0, 106069)\t0.3655545001090455\n",
            "  (0, 67828)\t0.26800375270827315\n",
            "  (0, 31168)\t0.16247724180521766\n",
            "  (0, 15110)\t0.1719352837797837\n",
            "  (1, 366203)\t0.24595562404108307\n",
            "  (1, 348135)\t0.4739279595416274\n",
            "  (1, 256777)\t0.28751585696559306\n",
            "  (1, 217562)\t0.40288153995289894\n",
            "  (1, 145393)\t0.575262969264869\n",
            "  (1, 15110)\t0.211037449588008\n",
            "  (1, 6463)\t0.30733520460524466\n",
            "  (2, 400621)\t0.4317732461913093\n",
            "  (2, 256834)\t0.2564939661498776\n",
            "  (2, 183312)\t0.5892069252021465\n",
            "  (2, 89448)\t0.36340369428387626\n",
            "  (2, 34401)\t0.37916255084357414\n",
            "  :\t:\n",
            "  (319994, 123278)\t0.4530341382559843\n",
            "  (319995, 444934)\t0.3211092817599261\n",
            "  (319995, 420984)\t0.22631428606830145\n",
            "  (319995, 416257)\t0.23816465111736276\n",
            "  (319995, 324496)\t0.3613167933647574\n",
            "  (319995, 315813)\t0.28482299145634127\n",
            "  (319995, 296662)\t0.39924856793840147\n",
            "  (319995, 232891)\t0.25741278545890767\n",
            "  (319995, 213324)\t0.2683969144317078\n",
            "  (319995, 155493)\t0.2770682832971668\n",
            "  (319995, 109379)\t0.30208964848908326\n",
            "  (319995, 107868)\t0.3339934973754696\n",
            "  (319996, 438709)\t0.4143006291901984\n",
            "  (319996, 397506)\t0.9101400928717545\n",
            "  (319997, 444770)\t0.2668297951055569\n",
            "  (319997, 416695)\t0.29458327588067873\n",
            "  (319997, 349904)\t0.32484594100566083\n",
            "  (319997, 288421)\t0.48498483387153407\n",
            "  (319997, 261286)\t0.37323893626855326\n",
            "  (319997, 169411)\t0.403381646999604\n",
            "  (319997, 98792)\t0.4463892055808332\n",
            "  (319998, 438748)\t0.719789181620468\n",
            "  (319998, 130192)\t0.6941927210956169\n",
            "  (319999, 400636)\t0.2874420848216212\n",
            "  (319999, 389755)\t0.9577980203954275\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-U_YLvLHAhV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the Machine Learning Model"
      ],
      "metadata": {
        "id": "JYYR_tuyArUA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression"
      ],
      "metadata": {
        "id": "FmCVFr6QA3Gs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(max_iter=1000)"
      ],
      "metadata": {
        "id": "OlB5tlteA9MV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "NfsXwB5OBIC8",
        "outputId": "0b7d0d41-339c-4e4b-e956-c08e889738c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation\n",
        "\n",
        "Acurracy Score"
      ],
      "metadata": {
        "id": "PFJ1pEYHCyUr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy score on the training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "ig4nOHFNC4x2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TU1bxQG9BK_s",
        "outputId": "e05720e8-c765-46e3-a3e8-ac7477f07d68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.8102125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "f0Ty6wzWCwED"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy score on the test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "i35ioqrTDLFZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the test data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Za4iWMDDP4-",
        "outputId": "5f4c22b2-451a-4c5f-97fa-04c825dd0956"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the test data :  0.778021875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KtAaoKBdDUQJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Accuracy = 77.8%"
      ],
      "metadata": {
        "id": "P5CvDXE1Dc9a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving the trained model"
      ],
      "metadata": {
        "id": "Max_hd9_Dp1z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "5N7Vzj9xDg-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'trained_model.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "OCN854bTDw-S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the saved model\n",
        "loaded_model = pickle.load(open('trained_model.sav', 'rb'))"
      ],
      "metadata": {
        "id": "mHe-pVyKD6U8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X_test[220]\n",
        "\n",
        "prediction = loaded_model.predict(X_new)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0] == 0):\n",
        "  print('Negative Sentiment')\n",
        "else:\n",
        "  print('Positive Sentiment')"
      ],
      "metadata": {
        "id": "DXcj_285MwML",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1982471-0237-458a-88ec-56563a30ca54"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "Negative Sentiment\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ElO5frNDQhhg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X_test[200]\n",
        "print(Y_test[200])\n",
        "\n",
        "prediction = loaded_model.predict(X_new)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0] == 0):\n",
        "  print('Negative Sentiment')\n",
        "else:\n",
        "  print('Positive Sentiment'\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pmyrSn3SAs6",
        "outputId": "bc890703-828f-4cdb-ab5e-b76a96c52e83"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "[1]\n",
            "Positive Sentiment\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gESJAJQdVbUx"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPmrJrwV6Yp0TPPr6c+RTRP",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}