Airline_Sentiment_Analysis(90_Accuracy)_using_RNN.ipynb

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      "name": "python",
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    "colab": {
      "name": "Airline Sentiment Analysis(90% Accuracy) using RNN",
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
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    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = ':https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F17%2F742210%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240722%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240722T170156Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "id": "LqhXo1VEZJD9",
        "outputId": "2567ada2-2768-47fc-ccff-06ed8ee1c786",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading , 2678605 bytes compressed\n",
            "[==================================================] 2678605 bytes downloaded\n",
            "Downloaded and uncompressed: \n",
            "Data source import complete.\n"
          ]
        }
      ],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **A Bigineer NoteBook on NLP with RNN**\n",
        "# **Kindly Upvote**"
      ],
      "metadata": {
        "id": "HUJ6G6pcZJED"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Airline Sentiment Analysi**s is a natural language processing (NLP) task that involves identifying the sentiment expressed in a piece of text about an airline. Recurrent Neural Networks (RNNs) are a type of neural network that are particularly well-suited to modeling sequences of data, such as natural language text. In this task, we can use RNNs to classify tweets about airlines into positive, negative or neutral categories.\n",
        "\n",
        "Here is a high-level overview of the steps involved in building an airline sentiment analysis model using RNNs:\n",
        "\n",
        "1. Data Preprocessing: The first step is to prepare the data for training the RNN. This involves cleaning the text data by removing stop words, special characters, and URLs. We also tokenize the text data and convert it into numerical format.\n",
        "\n",
        "2. Feature Extraction: Next, we convert the tokenized text data into a fixed-length vector using techniques such as Word2Vec or GloVe.\n",
        "\n",
        "3. Model Building: We can build a Recurrent Neural Network (RNN) model to classify the sentiment of the airline tweets. We can use architectures such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) to model the sequence of the text data.\n",
        "\n",
        "4. Model Training: We can train the RNN model on the preprocessed and feature-extracted data. We can use techniques such as cross-validation to evaluate the model's performance.\n",
        "\n",
        "5. Model Testing: We can test the trained model on new, unseen data to evaluate its performance.\n",
        "\n",
        "**Here are the steps in more detail:**\n",
        "\n",
        "**Data Preprocessing:**\n",
        "We start by importing the necessary libraries for data cleaning and tokenization, such as NLTK and spaCy. We then load the airline tweets dataset and perform some initial data exploration. We can also remove any irrelevant or noisy data such as tweets that contain links or mentions of other Twitter users. After that, we perform text cleaning by removing stop words, special characters, and other noise from the text data. Finally, we tokenize the text data and convert it into numerical format for feature extraction.\n",
        "\n",
        "**Feature Extraction:**\n",
        "We can extract features from the tokenized text data using techniques such as Word2Vec or GloVe. These methods convert the text data into fixed-length vectors that can be fed into the RNN model. We can also use techniques such as tf-idf or bag-of-words to represent the text data.\n",
        "\n",
        "**Model Building:**\n",
        "We can build a Recurrent Neural Network (RNN) model to classify the sentiment of the airline tweets. We can use architectures such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) to model the sequence of the text data. We can also use techniques such as dropout and batch normalization to regularize the model and prevent overfitting.\n",
        "\n",
        "**Model Training:**\n",
        "We can train the RNN model on the preprocessed and feature-extracted data. We can use techniques such as cross-validation to evaluate the model's performance. We can also use techniques such as early stopping to prevent the model from overfitting the training data.\n",
        "\n",
        "**Model Testing:**\n",
        "We can test the trained model on new, unseen data to evaluate its performance. We can use metrics such as accuracy, precision, recall, and F1 score to evaluate the model's performance on the test data. We can also use techniques such as confusion matrix and ROC curve to analyze the model's performance in more detail.\n",
        "\n",
        "In summary, building an airline sentiment analysis model using RNNs involves data preprocessing, feature extraction, model building, model training, and model testing. With the right techniques and tools, we can build a robust and accurate model that can classify the sentiment of airline tweets with high accuracy."
      ],
      "metadata": {
        "id": "r9sXqB2uZJEF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Import the Neccesary Libraries**"
      ],
      "metadata": {
        "id": "a4ivQ4ErZJEG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import warnings\n",
        "import itertools\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:16.821009Z",
          "iopub.execute_input": "2023-03-01T15:53:16.821313Z",
          "iopub.status.idle": "2023-03-01T15:53:19.698105Z",
          "shell.execute_reply.started": "2023-03-01T15:53:16.82126Z",
          "shell.execute_reply": "2023-03-01T15:53:19.697176Z"
        },
        "trusted": true,
        "id": "u_FY0BM_ZJEH"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reading the Dataset"
      ],
      "metadata": {
        "id": "7tM-p13UZJEJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"../input/Tweets.csv\")\n",
        "df = data[[\"text\",\"airline_sentiment\"]]\n",
        "df['text'] = df['text'].map(lambda x: x.lstrip('@VirginAmerica@UnitedAir@Southwestairline@DeltaAir@USAirways@American').rstrip('@'))"
      ],
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:19.700166Z",
          "iopub.execute_input": "2023-03-01T15:53:19.700482Z",
          "iopub.status.idle": "2023-03-01T15:53:19.945383Z",
          "shell.execute_reply.started": "2023-03-01T15:53:19.700432Z",
          "shell.execute_reply": "2023-03-01T15:53:19.944242Z"
        },
        "trusted": true,
        "id": "HbPK531iZJEJ",
        "outputId": "b75cf13d-e657-4bd6-ee66-db51cf463d76",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-a917ea3854bf>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['text'] = df['text'].map(lambda x: x.lstrip('@VirginAmerica@UnitedAir@Southwestairline@DeltaAir@USAirways@American').rstrip('@'))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:19.946846Z",
          "iopub.execute_input": "2023-03-01T15:53:19.947344Z",
          "iopub.status.idle": "2023-03-01T15:53:19.975854Z",
          "shell.execute_reply.started": "2023-03-01T15:53:19.947287Z",
          "shell.execute_reply": "2023-03-01T15:53:19.974981Z"
        },
        "trusted": true,
        "id": "EFBXNN2qZJEK",
        "outputId": "1e49796b-7dea-46ce-e299-a3f4477a831d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text airline_sentiment\n",
              "0                                   What @dhepburn said.           neutral\n",
              "1       plus you've added commercials to the experien...          positive\n",
              "2       I didn't today... Must mean I need to take an...           neutral\n",
              "3       it's really aggressive to blast obnoxious \"en...          negative\n",
              "4               and it's a really big bad thing about it          negative\n",
              "...                                                  ...               ...\n",
              "14635   thank you we got on a different flight to Chi...          positive\n",
              "14636   leaving over 20 minutes Late Flight. No warni...          negative\n",
              "14637    Please bring American Airlines to #BlackBerry10           neutral\n",
              "14638   you have my money, you change my flight, and ...          negative\n",
              "14639   we have 8 ppl so we need 2 know how many seat...           neutral\n",
              "\n",
              "[14640 rows x 2 columns]"
            ],
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              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>airline_sentiment</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What @dhepburn said.</td>\n",
              "      <td>neutral</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>plus you've added commercials to the experien...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I didn't today... Must mean I need to take an...</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>it's really aggressive to blast obnoxious \"en...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>and it's a really big bad thing about it</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>14635</th>\n",
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              "    <tr>\n",
              "      <th>14636</th>\n",
              "      <td>leaving over 20 minutes Late Flight. No warni...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14637</th>\n",
              "      <td>Please bring American Airlines to #BlackBerry10</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14638</th>\n",
              "      <td>you have my money, you change my flight, and ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14639</th>\n",
              "      <td>we have 8 ppl so we need 2 know how many seat...</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14640 rows × 2 columns</p>\n",
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              "    </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-c4386b81-c8c6-429a-aec2-925aa507d327 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d5192db8-2b5c-4b2c-9de4-3f5bd017e2b4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d5192db8-2b5c-4b2c-9de4-3f5bd017e2b4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 14640,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14369,\n        \"samples\": [\n          \" I'd love to take my seat, but it appears as though someone already has.  #disappointed #upgrademe #wtf http://t.co/9Gx5mMmubb\",\n          \" is there a phone number or email address to customer service?\",\n          \" comedy of errors continues...\\\" We can't get into our gate, there is a broken down truck in the way\\\" #fail\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airline_sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"positive\",\n          \"negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data.airline);"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:19.97722Z",
          "iopub.execute_input": "2023-03-01T15:53:19.97772Z",
          "iopub.status.idle": "2023-03-01T15:53:20.283425Z",
          "shell.execute_reply.started": "2023-03-01T15:53:19.977666Z",
          "shell.execute_reply": "2023-03-01T15:53:20.282588Z"
        },
        "trusted": true,
        "id": "fDOSgccOZJEL",
        "outputId": "2097e7e2-e88f-4fe2-bbeb-6c0ad7ae13eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df.airline_sentiment!=\"neutral\"] # To remove neutral responses\n",
        "df['text'] = df['text'].apply(lambda x: x.lower()) # To lower\n",
        "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x))) # To keep numbers and strings only"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:20.286502Z",
          "iopub.execute_input": "2023-03-01T15:53:20.28699Z",
          "iopub.status.idle": "2023-03-01T15:53:20.520931Z",
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          "shell.execute_reply": "2023-03-01T15:53:20.520142Z"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-2e7e31eb758f>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['text'] = df['text'].apply(lambda x: x.lower()) # To lower\n",
            "<ipython-input-6-2e7e31eb758f>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x))) # To keep numbers and strings only\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5) #Quick Look"
      ],
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          "iopub.execute_input": "2023-03-01T15:53:20.52292Z",
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          "shell.execute_reply": "2023-03-01T15:53:20.534571Z"
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          "height": 206
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      "execution_count": 7,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                                text airline_sentiment\n",
              "1   plus youve added commercials to the experienc...          positive\n",
              "3   its really aggressive to blast obnoxious ente...          negative\n",
              "4            and its a really big bad thing about it          negative\n",
              "5   seriously would pay 30 a flight for seats tha...          negative\n",
              "6   yes nearly every time i fly vx this ear worm ...          positive"
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11541,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11306,\n        \"samples\": [\n          \" thanks  can you help remind the agents its ok  ps heard rumors of a streaming wifi tvmovie service you might be installing\",\n          \" 4 segments 44 delayed gnv gt ctl  ctl gt jan  jan gt ctl  ctl gt gnv my year off from flying with you guys was the way to go\",\n          \" is that the same reliable system couldnt find my info and then said it refund my credit card \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airline_sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df.airline_sentiment);#Mostly Negative Reviews(Class Imbalance found)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:20.536801Z",
          "iopub.execute_input": "2023-03-01T15:53:20.537274Z",
          "iopub.status.idle": "2023-03-01T15:53:20.737759Z",
          "shell.execute_reply.started": "2023-03-01T15:53:20.537062Z",
          "shell.execute_reply": "2023-03-01T15:53:20.736707Z"
        },
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        "id": "0e9grsN-ZJEM",
        "outputId": "bb42d2bd-dd95-4c0e-fe2d-c5b3961c1d7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This next line of code drops the first 5000 rows where the value of the \"airline_sentiment\" column is \"negative\" from a pandas DataFrame called \"df\", due to unequal amount of negatives to positives\n",
        "\n",
        "In summary, the code drops the first 5000 rows where the value of the \"airline_sentiment\" column is \"negative\" from the original DataFrame \"df\" and returns a new DataFrame without those rows."
      ],
      "metadata": {
        "id": "s5jPDnXCZJEM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(df[df.airline_sentiment == \"negative\"].iloc[:5000].index)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:20.739359Z",
          "iopub.execute_input": "2023-03-01T15:53:20.739655Z",
          "iopub.status.idle": "2023-03-01T15:53:20.753751Z",
          "shell.execute_reply.started": "2023-03-01T15:53:20.73961Z",
          "shell.execute_reply": "2023-03-01T15:53:20.752445Z"
        },
        "trusted": true,
        "id": "kQI0b_cUZJEN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df.airline_sentiment);"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:20.75578Z",
          "iopub.execute_input": "2023-03-01T15:53:20.756316Z",
          "iopub.status.idle": "2023-03-01T15:53:21.014734Z",
          "shell.execute_reply.started": "2023-03-01T15:53:20.756265Z",
          "shell.execute_reply": "2023-03-01T15:53:21.013595Z"
        },
        "trusted": true,
        "id": "sxiHtB8iZJEN",
        "outputId": "8f8be807-d6ae-43e0-a851-626fabace702",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code snippet uses the Keras Tokenizer and pad_sequences functions to preprocess text data. Specifically, it tokenizes the text data by splitting the text into individual words and then maps these words to integers. The maximum number of unique words to be considered is set to 4000 using the max_features parameter of the Tokenizer function.\n",
        "\n",
        "After the text is tokenized, the pad_sequences function is used to ensure that all sequences have the same length by adding padding to the beginning or end of the sequences as needed. The resulting X variable is a two-dimensional numpy array where each row represents a sequence of integers corresponding to the words in the original text.\n",
        "\n",
        "Overall, this code prepares the text data for use in a machine learning model by converting it into a format that can be easily processed by the model."
      ],
      "metadata": {
        "id": "dC0irVCDZJEN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_fatures = 4000\n",
        "tokenizer = Tokenizer(num_words = max_fatures, split=' ')\n",
        "tokenizer.fit_on_texts(df['text'].values)\n",
        "X = tokenizer.texts_to_sequences(df['text'].values)\n",
        "X = pad_sequences(X)\n",
        "Y = df['airline_sentiment']\n",
        "L = Y.values\n",
        "X\n",
        "L"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:21.016504Z",
          "iopub.execute_input": "2023-03-01T15:53:21.017106Z",
          "iopub.status.idle": "2023-03-01T15:53:21.410534Z",
          "shell.execute_reply.started": "2023-03-01T15:53:21.017049Z",
          "shell.execute_reply": "2023-03-01T15:53:21.409799Z"
        },
        "trusted": true,
        "id": "qZw4_v51ZJEO",
        "outputId": "febed4f0-bf08-4c6e-cbdf-4f32d8a7de3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['positive', 'positive', 'positive', ..., 'positive', 'negative',\n",
              "       'negative'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = []\n",
        "for i in range(6541):\n",
        "    if L[i]==\"negative\":\n",
        "                        k.append(0)\n",
        "    elif L[i]==\"positive\":\n",
        "                        k.append(1)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:21.412092Z",
          "iopub.execute_input": "2023-03-01T15:53:21.412378Z",
          "iopub.status.idle": "2023-03-01T15:53:21.419841Z",
          "shell.execute_reply.started": "2023-03-01T15:53:21.412332Z",
          "shell.execute_reply": "2023-03-01T15:53:21.418857Z"
        },
        "trusted": true,
        "id": "KfV9owqwZJEO"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train Test Split**"
      ],
      "metadata": {
        "id": "Q6xfyDeaZJEO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, k,\n",
        "                                                    test_size=0.3,\n",
        "                                                    shuffle = True,\n",
        "                                                    stratify = k,\n",
        "                                                    random_state = 1 )"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:21.421468Z",
          "iopub.execute_input": "2023-03-01T15:53:21.422068Z",
          "iopub.status.idle": "2023-03-01T15:53:21.43659Z",
          "shell.execute_reply.started": "2023-03-01T15:53:21.421863Z",
          "shell.execute_reply": "2023-03-01T15:53:21.435828Z"
        },
        "trusted": true,
        "id": "dzxn8ihZZJEO"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the Model"
      ],
      "metadata": {
        "id": "WqvBqMekZJEQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embed_dim = 128\n",
        "lstm_out = 196\n",
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Embedding(max_fatures, 128, input_length=X_train.shape[1]))\n",
        "model.add(tf.keras.layers.SpatialDropout1D(0.5))\n",
        "model.add(tf.keras.layers.LSTM(196, dropout = 0.3, recurrent_dropout = 0.3 ))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Dense(100, activation = tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dropout(0.4))\n",
        "model.add(tf.keras.layers.Dense(2, activation = tf.nn.softmax))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:21.438301Z",
          "iopub.execute_input": "2023-03-01T15:53:21.438762Z",
          "iopub.status.idle": "2023-03-01T15:53:22.077593Z",
          "shell.execute_reply.started": "2023-03-01T15:53:21.438561Z",
          "shell.execute_reply": "2023-03-01T15:53:22.076743Z"
        },
        "trusted": true,
        "id": "mC5NTQz9ZJER",
        "outputId": "e2e152b1-5b2a-4790-953f-0a2ebdcae697",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:22.082263Z",
          "iopub.execute_input": "2023-03-01T15:53:22.082562Z",
          "iopub.status.idle": "2023-03-01T15:53:22.161143Z",
          "shell.execute_reply.started": "2023-03-01T15:53:22.082506Z",
          "shell.execute_reply": "2023-03-01T15:53:22.160411Z"
        },
        "trusted": true,
        "id": "ibjcfwonZJER"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the Model"
      ],
      "metadata": {
        "id": "EjTItYZmZJER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "# Assuming X_train and Y_train are lists of integers, convert them to NumPy arrays:\n",
        "X_train = np.array(X_train)\n",
        "Y_train = np.array(Y_train)\n",
        "\n",
        "Model = model.fit(X_train,\n",
        "                 Y_train,\n",
        "                 epochs=20,\n",
        "                 batch_size=32,\n",
        "                 validation_split = 0.2, # Now this should work as expected\n",
        "                 verbose = 2)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Model = model.fit(X_train,\n",
        "#                          Y_train,\n",
        "#                          epochs=20,\n",
        "#                          batch_size=32,\n",
        "#                          validation_split = 0.2,\n",
        "#                          verbose = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:53:22.165485Z",
          "iopub.execute_input": "2023-03-01T15:53:22.167579Z",
          "iopub.status.idle": "2023-03-01T15:55:48.982776Z",
          "shell.execute_reply.started": "2023-03-01T15:53:22.167524Z",
          "shell.execute_reply": "2023-03-01T15:55:48.981737Z"
        },
        "trusted": true,
        "id": "RZTwk0E-ZJER",
        "outputId": "7e363402-0abb-4f65-df92-d9e3b5e6fd62",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "115/115 - 23s - loss: 0.4953 - accuracy: 0.7662 - val_loss: 0.2953 - val_accuracy: 0.8755 - 23s/epoch - 198ms/step\n",
            "Epoch 2/20\n",
            "115/115 - 12s - loss: 0.2190 - accuracy: 0.9126 - val_loss: 0.2307 - val_accuracy: 0.9094 - 12s/epoch - 102ms/step\n",
            "Epoch 3/20\n",
            "115/115 - 12s - loss: 0.1487 - accuracy: 0.9432 - val_loss: 0.2119 - val_accuracy: 0.9269 - 12s/epoch - 104ms/step\n",
            "Epoch 4/20\n",
            "115/115 - 12s - loss: 0.1031 - accuracy: 0.9629 - val_loss: 0.2282 - val_accuracy: 0.9192 - 12s/epoch - 106ms/step\n",
            "Epoch 5/20\n",
            "115/115 - 13s - loss: 0.0712 - accuracy: 0.9760 - val_loss: 0.2464 - val_accuracy: 0.9225 - 13s/epoch - 113ms/step\n",
            "Epoch 6/20\n",
            "115/115 - 11s - loss: 0.0641 - accuracy: 0.9754 - val_loss: 0.2876 - val_accuracy: 0.9181 - 11s/epoch - 99ms/step\n",
            "Epoch 7/20\n",
            "115/115 - 12s - loss: 0.0501 - accuracy: 0.9823 - val_loss: 0.3029 - val_accuracy: 0.9279 - 12s/epoch - 106ms/step\n",
            "Epoch 8/20\n",
            "115/115 - 12s - loss: 0.0280 - accuracy: 0.9910 - val_loss: 0.3088 - val_accuracy: 0.9148 - 12s/epoch - 102ms/step\n",
            "Epoch 9/20\n",
            "115/115 - 12s - loss: 0.0345 - accuracy: 0.9888 - val_loss: 0.3063 - val_accuracy: 0.9203 - 12s/epoch - 103ms/step\n",
            "Epoch 10/20\n",
            "115/115 - 12s - loss: 0.0202 - accuracy: 0.9934 - val_loss: 0.3087 - val_accuracy: 0.9148 - 12s/epoch - 102ms/step\n",
            "Epoch 11/20\n",
            "115/115 - 12s - loss: 0.0187 - accuracy: 0.9937 - val_loss: 0.3948 - val_accuracy: 0.9170 - 12s/epoch - 101ms/step\n",
            "Epoch 12/20\n",
            "115/115 - 11s - loss: 0.0110 - accuracy: 0.9965 - val_loss: 0.5487 - val_accuracy: 0.9017 - 11s/epoch - 98ms/step\n",
            "Epoch 13/20\n",
            "115/115 - 11s - loss: 0.0181 - accuracy: 0.9937 - val_loss: 0.4716 - val_accuracy: 0.9258 - 11s/epoch - 96ms/step\n",
            "Epoch 14/20\n",
            "115/115 - 11s - loss: 0.0113 - accuracy: 0.9965 - val_loss: 0.4260 - val_accuracy: 0.9192 - 11s/epoch - 96ms/step\n",
            "Epoch 15/20\n",
            "115/115 - 12s - loss: 0.0161 - accuracy: 0.9959 - val_loss: 0.4554 - val_accuracy: 0.9039 - 12s/epoch - 101ms/step\n",
            "Epoch 16/20\n",
            "115/115 - 12s - loss: 0.0152 - accuracy: 0.9937 - val_loss: 0.3911 - val_accuracy: 0.9181 - 12s/epoch - 104ms/step\n",
            "Epoch 17/20\n",
            "115/115 - 11s - loss: 0.0074 - accuracy: 0.9978 - val_loss: 0.4523 - val_accuracy: 0.9138 - 11s/epoch - 99ms/step\n",
            "Epoch 18/20\n",
            "115/115 - 11s - loss: 0.0059 - accuracy: 0.9973 - val_loss: 0.4897 - val_accuracy: 0.9203 - 11s/epoch - 99ms/step\n",
            "Epoch 19/20\n",
            "115/115 - 12s - loss: 0.0076 - accuracy: 0.9965 - val_loss: 0.5149 - val_accuracy: 0.9105 - 12s/epoch - 102ms/step\n",
            "Epoch 20/20\n",
            "115/115 - 11s - loss: 0.0297 - accuracy: 0.9934 - val_loss: 0.5189 - val_accuracy: 0.9094 - 11s/epoch - 99ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = np.array(X_test)\n",
        "Y_test = np.array(Y_test)\n",
        "score = model.evaluate(X_test, Y_test, verbose=False)\n",
        "print(\"loss = \",score[0])\n",
        "print(\"accuracy = \",score[1])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:55:48.984414Z",
          "iopub.execute_input": "2023-03-01T15:55:48.984772Z",
          "iopub.status.idle": "2023-03-01T15:55:50.245464Z",
          "shell.execute_reply.started": "2023-03-01T15:55:48.984717Z",
          "shell.execute_reply": "2023-03-01T15:55:50.244418Z"
        },
        "trusted": true,
        "id": "KJ9IZhg1ZJER",
        "outputId": "58d11c6d-b0c6-43b8-9b53-5db5495509b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss =  0.6205897927284241\n",
            "accuracy =  0.9021905064582825\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Plottiing the confusion matrix**"
      ],
      "metadata": {
        "id": "IqwlsRz_ZJER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_classes = np.argmax(y_pred,axis = 1)\n",
        "confusion_mtx = confusion_matrix(Y_test, y_pred_classes)\n",
        "plot_confusion_matrix(confusion_mtx, classes = range(2))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:55:50.247633Z",
          "iopub.execute_input": "2023-03-01T15:55:50.247958Z",
          "iopub.status.idle": "2023-03-01T15:55:52.013768Z",
          "shell.execute_reply.started": "2023-03-01T15:55:50.247907Z",
          "shell.execute_reply": "2023-03-01T15:55:52.012641Z"
        },
        "trusted": true,
        "id": "emU-k-0aZJER",
        "outputId": "d4580bcc-2830-4b38-c249-8a6f2d59de6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62/62 [==============================] - 1s 10ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Test"
      ],
      "metadata": {
        "id": "lcTMC4DVZJES"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample = ['Meetings: Air crew is so dumb.']\n",
        "sample = tokenizer.texts_to_sequences(sample)\n",
        "sample = pad_sequences(sample, maxlen=31, dtype='int32', value=0)\n",
        "print(sample)\n",
        "sentiment = model.predict(sample,batch_size=1,verbose = 2)[0]\n",
        "if(np.argmax(sentiment) == 0):\n",
        "    print(\"negative\")\n",
        "elif (np.argmax(sentiment) == 1):\n",
        "    print(\"positive\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-03-01T15:55:52.020432Z",
          "iopub.execute_input": "2023-03-01T15:55:52.023947Z",
          "iopub.status.idle": "2023-03-01T15:55:52.08716Z",
          "shell.execute_reply.started": "2023-03-01T15:55:52.023844Z",
          "shell.execute_reply": "2023-03-01T15:55:52.085865Z"
        },
        "trusted": true,
        "id": "PJhgkyLLZJES",
        "outputId": "9b335d80-2226-40b4-89d9-f5021923a154",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0 2030  237  147\n",
            "    11   40 2049]]\n",
            "1/1 - 0s - 88ms/epoch - 88ms/step\n",
            "negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: save and downlowad model into google drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# Change the directory to your desired location in Google Drive\n",
        "model_save_path = '/content/gdrive/My Drive/Colab Notebooks/my_model.h5'\n",
        "model.save(model_save_path)\n",
        "\n",
        "# Download the model file\n",
        "from google.colab import files\n",
        "files.download(model_save_path)\n"
      ],
      "metadata": {
        "id": "Bm6tK-9Ab1Dl",
        "outputId": "4e2d5485-efe6-495b-a27e-178eb5832c18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e17896b8-25d9-4681-b000-fd5b3f6c1ee4\", \"my_model.h5\", 9486960)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: make a streamlit application that take input text and then perform sentement analysis\n",
        "\n",
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "import tensorflow as tf\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "import numpy as np\n",
        "\n",
        "# Load the saved model\n",
        "model_path = 'my_model.h5'  # Update with your actual model path\n",
        "model = tf.keras.models.load_model(model_path)\n",
        "\n",
        "# Initialize the tokenizer (assuming same parameters as during training)\n",
        "max_fatures = 4000\n",
        "tokenizer = Tokenizer(num_words=max_fatures, split=' ')\n",
        "# You need to fit the tokenizer on the same data as during training\n",
        "# If you don't have that data readily available, you might need to\n",
        "# load a saved tokenizer or re-fit on a representative dataset.\n",
        "\n",
        "# Streamlit app\n",
        "st.title(\"Airline Sentiment Analysis\")\n",
        "\n",
        "user_input = st.text_area(\"Enter your tweet about an airline:\")\n",
        "\n",
        "if st.button(\"Analyze\"):\n",
        "    if user_input:\n",
        "        # Preprocess the input text\n",
        "        sample = tokenizer.texts_to_sequences([user_input])\n",
        "        sample = pad_sequences(sample, maxlen=31, dtype='int32', value=0)\n",
        "\n",
        "        # Predict sentiment\n",
        "        sentiment = model.predict(sample, batch_size=1, verbose=0)[0]\n",
        "        predicted_class = np.argmax(sentiment)\n",
        "\n",
        "        if predicted_class == 0:\n",
        "            st.write(\"Sentiment: Negative\")\n",
        "        elif predicted_class == 1:\n",
        "            st.write(\"Sentiment: Positive\")\n",
        "    else:\n",
        "        st.write(\"Please enter some text.\")\n",
        "\n",
        "!streamlit run app.py\n"
      ],
      "metadata": {
        "id": "GMFTQMWwceVN",
        "outputId": "8c841e26-d61a-4bd3-e915-797e5b555e98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: how to run application\n",
        "\n",
        "!streamlit run app.py\n"
      ],
      "metadata": {
        "id": "b70-OzNXdGP8",
        "outputId": "dfc2c340-be17-42b3-bbe6-c9e5e540bca4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: streamlit: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "-rHCRI7wc9L3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}