GRU_Sentiment_Analysis.ipynb

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      "display_name": "Python 3",
      "name": "python3"
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "sourceId": 7563141,
          "sourceType": "datasetVersion",
          "datasetId": 4403839
        }
      ],
      "dockerImageVersionId": 30673,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/singhsourav0/Deep-Learning-Odyssey/blob/main/GRU_Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "source": [
        "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 = 'emotions:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F4403839%2F7563141%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240413%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240413T175913Z%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": "N27r2QbFnRWq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5158d7c7-57a9-4c6c-ff2a-26cef7b6cabb"
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading emotions, 16440679 bytes compressed\n",
            "[==================================================] 16440679 bytes downloaded\n",
            "Downloaded and uncompressed: emotions\n",
            "Data source import complete.\n"
          ]
        }
      ],
      "execution_count": 1
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
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          "iopub.status.idle": "2024-04-01T04:21:55.61651Z",
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        "id": "2VMJXLTenRWu",
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        },
        "outputId": "b8c8a04e-2e50-4523-a160-21ea5262b6bc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/kaggle/input/emotions/text.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/kaggle/input/emotions/text.csv')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:21:55.618158Z",
          "iopub.execute_input": "2024-04-01T04:21:55.619121Z",
          "iopub.status.idle": "2024-04-01T04:21:57.036373Z",
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          "shell.execute_reply": "2024-04-01T04:21:57.035118Z"
        },
        "trusted": true,
        "id": "3k-iyy4znRWv"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from collections import Counter\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "from keras.preprocessing import sequence\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:21:57.038009Z",
          "iopub.execute_input": "2024-04-01T04:21:57.038693Z",
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          "shell.execute_reply": "2024-04-01T04:22:14.878987Z"
        },
        "trusted": true,
        "id": "zAmaza92nRWv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f60c417-7a62-48e3-fe62-d8f069ed25ba"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:14.882874Z",
          "iopub.execute_input": "2024-04-01T04:22:14.883622Z",
          "iopub.status.idle": "2024-04-01T04:22:14.904925Z",
          "shell.execute_reply.started": "2024-04-01T04:22:14.883581Z",
          "shell.execute_reply": "2024-04-01T04:22:14.903306Z"
        },
        "trusted": true,
        "id": "fcE3BIWHnRWw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "43cc2c00-3135-4a32-afc8-53db339a0433"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                               text  label\n",
              "0           0      i just feel really helpless and heavy hearted      4\n",
              "1           1  ive enjoyed being able to slouch about relax a...      0\n",
              "2           2  i gave up my internship with the dmrg and am f...      4\n",
              "3           3                         i dont know i feel so lost      0\n",
              "4           4  i am a kindergarten teacher and i am thoroughl...      4"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    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-868e860d-b12b-4103-9db3-6c64f9086288 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": "df"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:14.906446Z",
          "iopub.execute_input": "2024-04-01T04:22:14.906839Z",
          "iopub.status.idle": "2024-04-01T04:22:15.228233Z",
          "shell.execute_reply.started": "2024-04-01T04:22:14.906805Z",
          "shell.execute_reply": "2024-04-01T04:22:15.227099Z"
        },
        "trusted": true,
        "id": "xnj4aEvonRWw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "142393c0-3589-4fef-c280-0badd3ea2b99"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:15.229407Z",
          "iopub.execute_input": "2024-04-01T04:22:15.229736Z",
          "iopub.status.idle": "2024-04-01T04:22:15.550537Z",
          "shell.execute_reply.started": "2024-04-01T04:22:15.22971Z",
          "shell.execute_reply": "2024-04-01T04:22:15.549126Z"
        },
        "trusted": true,
        "id": "IQmCOxzTnRWw"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotion_map = {\n",
        "    0: 'sadness',\n",
        "    1: 'joy',\n",
        "    2: 'love',\n",
        "    3: 'anger',\n",
        "    4: 'fear',\n",
        "    5: 'surprise'\n",
        "\n",
        "}\n",
        "\n",
        "df['label'] = df['label'].map(emotion_map)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:15.552119Z",
          "iopub.execute_input": "2024-04-01T04:22:15.552592Z",
          "iopub.status.idle": "2024-04-01T04:22:15.580772Z",
          "shell.execute_reply.started": "2024-04-01T04:22:15.552549Z",
          "shell.execute_reply": "2024-04-01T04:22:15.579311Z"
        },
        "trusted": true,
        "id": "bD2VcMA1nRWw"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "count=df['label'].value_counts()\n",
        "sns.barplot(x=count.index, y=count.values)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:15.582434Z",
          "iopub.execute_input": "2024-04-01T04:22:15.582829Z",
          "iopub.status.idle": "2024-04-01T04:22:15.874124Z",
          "shell.execute_reply.started": "2024-04-01T04:22:15.582797Z",
          "shell.execute_reply": "2024-04-01T04:22:15.872529Z"
        },
        "trusted": true,
        "id": "25POy6YrnRWx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "384efd85-556b-4839-a042-0f4d257c6ec4"
      },
      "execution_count": 9,
      "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['text'] = df['text'].apply(lambda x: re.sub(r'[^a-zA-Z\\s]', '', x))\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "execution": {
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              "   Unnamed: 0                                               text    label\n",
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    {
      "cell_type": "code",
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        "stop = stopwords.words('english')\n",
        "df[\"text\"] = df['text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
        "\n",
        "df.head()"
      ],
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              "   Unnamed: 0                                               text    label\n",
              "0           0                 feel really helpless heavy hearted     fear\n",
              "1           1  ive enjoyed able slouch relax unwind frankly n...  sadness\n",
              "2           2            gave internship dmrg feeling distraught     fear\n",
              "3           3                                dont know feel lost  sadness\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    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-7bbc5685-6d59-4bb2-b3e4-62ffb312d625 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": "df"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'] = df['text'].str.replace(r'\\d+', '', regex=True)\n",
        "df['text'] = df['text'].str.lower()\n",
        "print(df.head())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:31.177559Z",
          "iopub.execute_input": "2024-04-01T04:22:31.178123Z",
          "iopub.status.idle": "2024-04-01T04:22:32.198707Z",
          "shell.execute_reply.started": "2024-04-01T04:22:31.178077Z",
          "shell.execute_reply": "2024-04-01T04:22:32.19774Z"
        },
        "trusted": true,
        "id": "fsAlbpYqnRWz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1183001-7a8c-444f-ce80-297f391d248a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0                                               text    label\n",
            "0           0                 feel really helpless heavy hearted     fear\n",
            "1           1  ive enjoyed able slouch relax unwind frankly n...  sadness\n",
            "2           2            gave internship dmrg feeling distraught     fear\n",
            "3           3                                dont know feel lost  sadness\n",
            "4           4  kindergarten teacher thoroughly weary job take...     fear\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'] = df['text'].str.replace(r'[^\\w\\s]', '', regex=True)\n",
        "df['text'] = df['text'].str.replace(r'http\\S+', '', regex=True)\n",
        "df.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:32.200184Z",
          "iopub.execute_input": "2024-04-01T04:22:32.20054Z",
          "iopub.status.idle": "2024-04-01T04:22:33.06823Z",
          "shell.execute_reply.started": "2024-04-01T04:22:32.200509Z",
          "shell.execute_reply": "2024-04-01T04:22:33.067063Z"
        },
        "trusted": true,
        "id": "D2wqNxGdnRWz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "1a87a31b-7e97-4c95-ef7d-a0f5b73cbf49"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                               text    label\n",
              "0           0                 feel really helpless heavy hearted     fear\n",
              "1           1  ive enjoyed able slouch relax unwind frankly n...  sadness\n",
              "2           2            gave internship dmrg feeling distraught     fear\n",
              "3           3                                dont know feel lost  sadness\n",
              "4           4  kindergarten teacher thoroughly weary job take...     fear"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef886b18-500e-4d03-81c7-c9d686f68d09\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>feel really helpless heavy hearted</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>ive enjoyed able slouch relax unwind frankly n...</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>gave internship dmrg feeling distraught</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>dont know feel lost</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>kindergarten teacher thoroughly weary job take...</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef886b18-500e-4d03-81c7-c9d686f68d09')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ef886b18-500e-4d03-81c7-c9d686f68d09 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-ef886b18-500e-4d03-81c7-c9d686f68d09');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6d132bcd-101f-46dd-ac35-2fc1f58188a6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d132bcd-101f-46dd-ac35-2fc1f58188a6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6d132bcd-101f-46dd-ac35-2fc1f58188a6 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": "df"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_mapping = {'sadness':0, 'joy':1, 'love':2, 'anger':3, 'fear':4,'surprise':5}\n",
        "\n",
        "\n",
        "df['label'] = df['label'].map(label_mapping)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:33.069992Z",
          "iopub.execute_input": "2024-04-01T04:22:33.070469Z",
          "iopub.status.idle": "2024-04-01T04:22:33.101031Z",
          "shell.execute_reply.started": "2024-04-01T04:22:33.070425Z",
          "shell.execute_reply": "2024-04-01T04:22:33.099465Z"
        },
        "trusted": true,
        "id": "r-W1pSWznRWz"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X=df['text']\n",
        "y=df['label']\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:33.102444Z",
          "iopub.execute_input": "2024-04-01T04:22:33.10281Z",
          "iopub.status.idle": "2024-04-01T04:22:33.189768Z",
          "shell.execute_reply.started": "2024-04-01T04:22:33.102779Z",
          "shell.execute_reply": "2024-04-01T04:22:33.188396Z"
        },
        "trusted": true,
        "id": "kENgq140nRW0"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=60000)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "tokenizer.fit_on_texts(X_test)\n",
        "X_train_sequences = tokenizer.texts_to_sequences(X_train)\n",
        "X_test_sequences = tokenizer.texts_to_sequences(X_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:33.191555Z",
          "iopub.execute_input": "2024-04-01T04:22:33.191922Z",
          "iopub.status.idle": "2024-04-01T04:22:48.50772Z",
          "shell.execute_reply.started": "2024-04-01T04:22:33.191893Z",
          "shell.execute_reply": "2024-04-01T04:22:48.506102Z"
        },
        "trusted": true,
        "id": "NgdM9GblnRW0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "maxlen = max(len(tokens) for tokens in X_train_sequences)\n",
        "print(\"Maximum sequence length (maxlen):\", maxlen)\n",
        "X_train_padded = pad_sequences(X_train_sequences, maxlen=maxlen, padding='post')\n",
        "X_test_padded = pad_sequences(X_test_sequences, maxlen=maxlen, padding='post')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:48.509549Z",
          "iopub.execute_input": "2024-04-01T04:22:48.509965Z",
          "iopub.status.idle": "2024-04-01T04:22:49.759065Z",
          "shell.execute_reply.started": "2024-04-01T04:22:48.509924Z",
          "shell.execute_reply": "2024-04-01T04:22:49.757791Z"
        },
        "trusted": true,
        "id": "eVexxVQXnRW0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96b1395c-8ed4-41ab-bad8-c64543785a54"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum sequence length (maxlen): 79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_size = np.max(X_train_padded) + 1\n",
        "input_size"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:49.760843Z",
          "iopub.execute_input": "2024-04-01T04:22:49.761273Z",
          "iopub.status.idle": "2024-04-01T04:22:49.78072Z",
          "shell.execute_reply.started": "2024-04-01T04:22:49.761237Z",
          "shell.execute_reply": "2024-04-01T04:22:49.779319Z"
        },
        "trusted": true,
        "id": "OLSIKNmknRW0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c733dbe-aa5e-4a31-82bf-e25caa08471f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "# Add an embedding layer\n",
        "model.add(Embedding(input_dim=input_size, output_dim=100,input_shape=(79,)))\n",
        "\n",
        "# Add a bidirectional GRU layer with 128 units\n",
        "model.add(Bidirectional(LSTM(128)))\n",
        "\n",
        "# Add batch normalization layer\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Add dropout regularization\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# Add a dense layer with 64 units and ReLU activation\n",
        "model.add(Dense(64, activation='relu'))\n",
        "\n",
        "# Add dropout regularization\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# Add the output layer with 6 units for 6 labels and softmax activation\n",
        "model.add(Dense(6, activation='softmax'))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:49.78264Z",
          "iopub.execute_input": "2024-04-01T04:22:49.782991Z",
          "iopub.status.idle": "2024-04-01T04:22:50.056077Z",
          "shell.execute_reply.started": "2024-04-01T04:22:49.782961Z",
          "shell.execute_reply": "2024-04-01T04:22:50.054791Z"
        },
        "trusted": true,
        "id": "aSNJde1lnRW1"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Display the model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:22:50.057684Z",
          "iopub.execute_input": "2024-04-01T04:22:50.058214Z",
          "iopub.status.idle": "2024-04-01T04:22:50.101693Z",
          "shell.execute_reply.started": "2024-04-01T04:22:50.058169Z",
          "shell.execute_reply": "2024-04-01T04:22:50.100547Z"
        },
        "trusted": true,
        "id": "K2SRtlWxnRW1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51e5d357-f9b9-4393-f979-00e4a3a21a89"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 79, 100)           6000000   \n",
            "                                                                 \n",
            " bidirectional (Bidirection  (None, 256)               234496    \n",
            " al)                                                             \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 256)               1024      \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 390       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6252358 (23.85 MB)\n",
            "Trainable params: 6251846 (23.85 MB)\n",
            "Non-trainable params: 512 (2.00 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_padded, y_train, epochs=3, batch_size=32, validation_data=(X_test_padded, y_test),callbacks=[EarlyStopping(patience=3)])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T04:25:41.674773Z",
          "iopub.execute_input": "2024-04-01T04:25:41.675501Z",
          "iopub.status.idle": "2024-04-01T05:42:34.961992Z",
          "shell.execute_reply.started": "2024-04-01T04:25:41.675443Z",
          "shell.execute_reply": "2024-04-01T05:42:34.9608Z"
        },
        "trusted": true,
        "id": "3tOzg8nRnRW2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76ebf1ee-1bba-4aa7-e0af-6b631b2b6a3e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "10421/10421 [==============================] - 177s 16ms/step - loss: 0.2261 - accuracy: 0.9049 - val_loss: 0.0983 - val_accuracy: 0.9397\n",
            "Epoch 2/3\n",
            "10421/10421 [==============================] - 160s 15ms/step - loss: 0.1063 - accuracy: 0.9377 - val_loss: 0.0947 - val_accuracy: 0.9405\n",
            "Epoch 3/3\n",
            "10421/10421 [==============================] - 140s 13ms/step - loss: 0.0999 - accuracy: 0.9398 - val_loss: 0.0930 - val_accuracy: 0.9408\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "background_color = '#5fa1bc'\n",
        "sns.set_theme(style=\"whitegrid\", rc={\"axes.facecolor\": background_color})\n",
        "\n",
        "# Get the epoch with the highest validation accuracy\n",
        "best_epoch = history.history['val_accuracy'].index(max(history.history['val_accuracy'])) + 1\n",
        "\n",
        "# Create a subplot with 1 row and 2 columns\n",
        "fig, axs = plt.subplots(1, 2, figsize=(16, 5))\n",
        "\n",
        "# Plot training and validation accuracy\n",
        "axs[0].plot(history.history['accuracy'], label='Training Accuracy', color='blue')\n",
        "axs[0].plot(history.history['val_accuracy'], label='Validation Accuracy', color='red')\n",
        "axs[0].scatter(best_epoch - 1, history.history['val_accuracy'][best_epoch - 1], color='green', label=f'Best Epoch: {best_epoch}')\n",
        "axs[0].set_xlabel('Epoch')\n",
        "axs[0].set_ylabel('Accuracy')\n",
        "axs[0].set_title('Training and Validation Accuracy')\n",
        "axs[0].legend()\n",
        "\n",
        "# Plot training and validation loss\n",
        "axs[1].plot(history.history['loss'], label='Training Loss', color='blue')\n",
        "axs[1].plot(history.history['val_loss'], label='Validation Loss', color='red')\n",
        "axs[1].scatter(best_epoch - 1, history.history['val_loss'][best_epoch - 1], color='green',label=f'Best Epoch: {best_epoch}')\n",
        "axs[1].set_xlabel('Epoch')\n",
        "axs[1].set_ylabel('Loss')\n",
        "axs[1].set_title('Training and Validation Loss')\n",
        "axs[1].legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T05:46:18.369104Z",
          "iopub.execute_input": "2024-04-01T05:46:18.36961Z",
          "iopub.status.idle": "2024-04-01T05:46:19.451644Z",
          "shell.execute_reply.started": "2024-04-01T05:46:18.369575Z",
          "shell.execute_reply": "2024-04-01T05:46:19.450343Z"
        },
        "trusted": true,
        "id": "sqTf5Z1anRW2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "16371c6b-e9a1-47cf-b8e6-5ce38993e65c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test_padded, y_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-01T05:46:37.829467Z",
          "iopub.execute_input": "2024-04-01T05:46:37.829912Z",
          "iopub.status.idle": "2024-04-01T05:48:59.868175Z",
          "shell.execute_reply.started": "2024-04-01T05:46:37.829875Z",
          "shell.execute_reply": "2024-04-01T05:48:59.867254Z"
        },
        "trusted": true,
        "id": "IFGcl5qgnRW2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3072837d-d238-45d2-9166-1e53a30d0e34"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2606/2606 [==============================] - 13s 5ms/step - loss: 0.0930 - accuracy: 0.9408\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.09298932552337646, 0.940752387046814]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"I am so bad today!\"\n",
        "# Convert the text to a sequence\n",
        "text_sequence = tokenizer.texts_to_sequences([text])\n",
        "# Pad the sequence\n",
        "padded_sequence = pad_sequences(text_sequence, maxlen=maxlen, padding='post')\n",
        "# Predict the emotion\n",
        "predicted_emotion = np.argmax(model.predict(padded_sequence))\n",
        "# Print the predicted emotion\n",
        "print(f\"Predicted emotion: {emotion_map[predicted_emotion]}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neBZ1ItkqfXF",
        "outputId": "fbf4a0c7-3f67-4abf-826d-268b9ee4959a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 20ms/step\n",
            "Predicted emotion: sadness\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sYBUpxJtqlyV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}