Sentiment_Analysis.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Abhinaycs/Social-Media-Sentiment-Analysis/blob/main/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": "RBzcoJ2nOqcl",
        "outputId": "3b319dce-a898-492c-bfea-effe6b41b08e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.7.4)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.8)\n"
          ]
        }
      ],
      "source": [
        "! pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Syp6X3PsRBLH"
      },
      "outputs": [],
      "source": [
        "# configuring the path of Kaggle.json file\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "rTEvFZ54RjDU",
        "outputId": "c874d306-2a7c-4733-ceba-7229685a6f22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/kazanova/sentiment140\n",
            "License(s): other\n",
            "sentiment140.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d kazanova/sentiment140"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "UOafzDliXwbR",
        "outputId": "39d39896-eea5-4a26-b43d-3406b30bcc4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dataset is extracted\n"
          ]
        }
      ],
      "source": [
        "from zipfile import ZipFile\n",
        "dataset = '/content/sentiment140.zip'\n",
        "\n",
        "with ZipFile(dataset,'r') as zip:\n",
        "  zip.extractall()\n",
        "  print('The dataset is extracted')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PAOlmamlYAu3"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "from nltk.corpus import stopwords\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2sataoyZ_MD",
        "outputId": "a71deefb-904e-40d9-d358-cb6ddda6386e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exMxOE_FbL0O",
        "outputId": "8c5af1cd-d8ec-4458-c8a5-91f99956110e"
      },
      "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(stopwords.words('english'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H1DGBqo8bPgG"
      },
      "outputs": [],
      "source": [
        "twitter_data=pd.read_csv('/content/training.1600000.processed.noemoticon.csv',encoding='ISO-8859-1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z32sR53_bkNE",
        "outputId": "80e16e4e-b05c-4b6b-a009-d4f4ea12e230"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599999, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "twitter_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G1o1u3IOb5y2"
      },
      "outputs": [],
      "source": [
        "column_names=['target','ids','date','flag','user','text']\n",
        "twitter_data=pd.read_csv('/content/training.1600000.processed.noemoticon.csv',names=column_names,encoding='ISO-8859-1')"
      ]
    },
    {
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            "text/plain": [
              "   target         ids                          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....  "
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              "      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-516d2209-2c58-40fb-a2af-074bcc7aba56 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": 79
        }
      ],
      "source": [
        "twitter_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "tpXBvYuacbt5",
        "outputId": "edfdd4b3-6ca6-49a4-d8f6-04e2c635d7d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target    0\n",
              "ids       0\n",
              "date      0\n",
              "flag      0\n",
              "user      0\n",
              "text      0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "twitter_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "FN6jxzBldB8d",
        "outputId": "05c7a231-22ff-493c-f708-79f9fed12679"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "4    800000\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 81
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      ],
      "source": [
        "twitter_data['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KnxX_72ZdMhS"
      },
      "outputs": [],
      "source": [
        "twitter_data.replace({'target':{4:1}},inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "9C8xZQqOdoQb",
        "outputId": "850315ca-86f4-4b94-ac7a-7f5bab89aca6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "0    800000\n",
              "1    800000\n",
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              "      <td>800000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 83
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      ],
      "source": [
        "twitter_data['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1zQJtXPGeL9d"
      },
      "outputs": [],
      "source": [
        "port_stem=PorterStemmer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fg5jXb_9duKu"
      },
      "outputs": [],
      "source": [
        "def stemming(content):\n",
        "    stemmed_content = re.sub('[^a-zA-Z]',' ', content) # Indent this line\n",
        "    stemmed_content = stemmed_content.lower()        # Indent this line\n",
        "    stemmed_content = stemmed_content.split()       # Indent this line\n",
        "    stemmed_content = [port_stem.stem(word) for word in stemmed_content if not word in stopwords.words('english')] # Indent this line\n",
        "    stemmed_content = ' '.join(stemmed_content)\n",
        "\n",
        "    return stemmed_content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oWdbN6jlfEgt"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "twitter_data['stemmed_content']=twitter_data['text'].apply(stemming)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N2nfAMrtfRSe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6b4dce08-0444-494d-ffdc-898076f418ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target         ids                          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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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "    }\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fdcd1d75-3c35-44da-94bb-392cf04201ba 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-fdcd1d75-3c35-44da-94bb-392cf04201ba');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "\n",
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              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      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-58f96895-b835-439a-b436-eb5878bf4cb7 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": 90
        }
      ],
      "source": [
        "twitter_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(twitter_data['stemmed_content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCfbRPEGinsj",
        "outputId": "ac251431-3243-4543-ae12-c2f5810493a1"
      },
      "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": "FefPRRWcmXAn",
        "outputId": "6bef56e7-e4f5-45cf-af7e-3f2a72fecf1c"
      },
      "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": [
        "X=twitter_data['stemmed_content'].values\n",
        "Y=twitter_data['target'].values"
      ],
      "metadata": {
        "id": "oFLrJVs9mjAp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBmDt48fmp6Y",
        "outputId": "4fdd8183-0acf-42c4-d40d-26a375b284f7"
      },
      "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": "ACxhaCfmmr9a",
        "outputId": "52846827-eb51-493d-a2d3-5861701e62af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,stratify=Y,random_state=2)"
      ],
      "metadata": {
        "id": "i7c6ZYH3mxLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OGsS32cnm9Bf",
        "outputId": "634b146e-514c-40de-ecb6-2b1a3106cb2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1600000,)\n",
            "(1280000,)\n",
            "(320000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbJcKtgGnBOY",
        "outputId": "bf245cbf-d78d-4123-b438-971ce22c957a"
      },
      "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(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_xcc5fCHnIB1",
        "outputId": "679897bf-495b-4828-dd8f-f51a7fb05e11"
      },
      "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": [
        "vectorizer=TfidfVectorizer()\n",
        "X_train=vectorizer.fit_transform(X_train)\n",
        "X_test=vectorizer.transform(X_test)"
      ],
      "metadata": {
        "id": "w58EiLTnnZKC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVTQ3vhnz9XL",
        "outputId": "51e42f51-234a-42bd-af67-29d32392d918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 443066)\t0.4484755317023172\n",
            "  (0, 235045)\t0.41996827700291095\n",
            "  (0, 109306)\t0.3753708587402299\n",
            "  (0, 185193)\t0.5277679060576009\n",
            "  (0, 354543)\t0.3588091611460021\n",
            "  (0, 436713)\t0.27259876264838384\n",
            "  (1, 160636)\t1.0\n",
            "  (2, 288470)\t0.16786949597862733\n",
            "  (2, 132311)\t0.2028971570399794\n",
            "  (2, 150715)\t0.18803850583207948\n",
            "  (2, 178061)\t0.1619010109445149\n",
            "  (2, 409143)\t0.15169282335109835\n",
            "  (2, 266729)\t0.24123230668976975\n",
            "  (2, 443430)\t0.3348599670252845\n",
            "  (2, 77929)\t0.31284080750346344\n",
            "  (2, 433560)\t0.3296595898028565\n",
            "  (2, 406399)\t0.32105459490875526\n",
            "  (2, 129411)\t0.29074192727957143\n",
            "  (2, 407301)\t0.18709338684973031\n",
            "  (2, 124484)\t0.1892155960801415\n",
            "  (2, 109306)\t0.4591176413728317\n",
            "  (3, 172421)\t0.37464146922154384\n",
            "  (3, 411528)\t0.27089772444087873\n",
            "  (3, 388626)\t0.3940776331458846\n",
            "  (3, 56476)\t0.5200465453608686\n",
            "  :\t:\n",
            "  (1279996, 390130)\t0.22064742191076112\n",
            "  (1279996, 434014)\t0.2718945052332447\n",
            "  (1279996, 318303)\t0.21254698865277746\n",
            "  (1279996, 237899)\t0.2236567560099234\n",
            "  (1279996, 291078)\t0.17981734369155505\n",
            "  (1279996, 412553)\t0.18967045002348676\n",
            "  (1279997, 112591)\t0.7574829183045267\n",
            "  (1279997, 273084)\t0.4353549002982409\n",
            "  (1279997, 5685)\t0.48650358607431304\n",
            "  (1279998, 385313)\t0.4103285865588191\n",
            "  (1279998, 275288)\t0.38703346602729577\n",
            "  (1279998, 162047)\t0.34691726958159064\n",
            "  (1279998, 156297)\t0.3137096161546449\n",
            "  (1279998, 153281)\t0.28378968751027456\n",
            "  (1279998, 435463)\t0.2851807874350361\n",
            "  (1279998, 124765)\t0.32241752985927996\n",
            "  (1279998, 169461)\t0.2659980990397061\n",
            "  (1279998, 93795)\t0.21717768937055476\n",
            "  (1279998, 412553)\t0.2816582375021589\n",
            "  (1279999, 96224)\t0.5416162421321443\n",
            "  (1279999, 135384)\t0.6130934129868719\n",
            "  (1279999, 433612)\t0.3607341026233411\n",
            "  (1279999, 435572)\t0.31691096877786484\n",
            "  (1279999, 31410)\t0.248792678366695\n",
            "  (1279999, 242268)\t0.19572649660865402\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5qpH4fv0ENr",
        "outputId": "d9df9029-ce0a-442d-e331-723f09f0310d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (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": [
        "model=LogisticRegression(max_iter=1000)"
      ],
      "metadata": {
        "id": "YDgZua_t0H-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "DJgk8_zS0RE7",
        "outputId": "e01c2adc-a7c0-4183-8ee4-f12f972eab95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#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": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_prediction=model.predict(X_train)\n",
        "training_data_accuracy=accuracy_score(Y_train,X_train_prediction)"
      ],
      "metadata": {
        "id": "LMZ8Xrze0WTT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score on the training date:',training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyQF2Vqb0s6m",
        "outputId": "dd778240-dff0-4886-f8da-9063cae1b0ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score on the training date: 0.81018984375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_prediction=model.predict(X_test)\n",
        "test_data_accuracy=accuracy_score(Y_test,X_test_prediction)"
      ],
      "metadata": {
        "id": "031v5cvN1K8m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "xNMe7gLN1S39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filename='trained_model.sav'\n",
        "pickle.dump(model,open(filename,'wb'))"
      ],
      "metadata": {
        "id": "W5xsRUhv1jcy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model=pickle.load(open(filename,'rb'))"
      ],
      "metadata": {
        "id": "3-mmfL5g1syK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new=X_test[200]\n",
        "print(Y_test[200])\n",
        "prediction=model.predict(X_new)\n",
        "print(prediction)\n",
        "if(prediction[0]==0):\n",
        "  print('Negative Tweet')\n",
        "else:\n",
        "  print('Positive Tweet')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0tSTlrv11cb",
        "outputId": "f4c2dde3-2998-4055-cb52-9747385a86cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "[1]\n",
            "Positive Tweet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new=X_test[3]\n",
        "print(Y_test[3])\n",
        "prediction=model.predict(X_new)\n",
        "print(prediction)\n",
        "if(prediction[0]==0):\n",
        "  print('Negative Tweet')\n",
        "else:\n",
        "  print('Positive Tweet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTsTeq9W2YVK",
        "outputId": "b0256a60-414c-47b2-977a-77b08d2f7570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "[0]\n",
            "Negative Tweet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_KQCYh20_7fe"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOmgj3bEzy66t3YXCaO4uZZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}