Tweet_Emotion_Recognition_MLproject.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhishek-38/ML/blob/main/Tweet_Emotion_Recognition_MLproject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp7D0ktn5eiG"
      },
      "source": [
        "## Tweet Emotion Recognition: Natural Language Processing with TensorFlow\n",
        "\n",
        "---\n",
        "\n",
        "Dataset: [Tweet Emotion Dataset](https://github.com/dair-ai/emotion_dataset)\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cprXxkrMxIgT"
      },
      "source": [
        "##  Setup and Imports\n",
        "\n",
        "1. Installing Hugging Face's nlp package\n",
        "2. Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5agZRy-45i0g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bdef8df-cf3c-48d4-8687-731c7824be27"
      },
      "source": [
        "!pip install nlp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting nlp\n",
            "  Downloading nlp-0.4.0-py3-none-any.whl (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 32.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from nlp) (3.7.1)\n",
            "Requirement already satisfied: pyarrow>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from nlp) (6.0.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from nlp) (1.3.5)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from nlp) (0.3.5.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from nlp) (2.23.0)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[K     |████████████████████████████████| 212 kB 71.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nlp) (1.21.6)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from nlp) (4.64.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2.10)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->nlp) (1.15.0)\n",
            "Installing collected packages: xxhash, nlp\n",
            "Successfully installed nlp-0.4.0 xxhash-3.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKFjWz6e5eiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6754747-d732-4910-9d71-21b1b059d3c9"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nlp\n",
        "import random\n",
        "\n",
        "\n",
        "def show_history(h):\n",
        "    epochs_trained = len(h.history['loss'])\n",
        "    plt.figure(figsize=(16, 6))\n",
        "\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('accuracy'), label='Training')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_accuracy'), label='Validation')\n",
        "    plt.ylim([0., 1.])\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('loss'), label='Training')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_loss'), label='Validation')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "def show_confusion_matrix(y_true, y_pred, classes):\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    \n",
        "    cm = confusion_matrix(y_true, y_pred, normalize='true')\n",
        "\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    sp = plt.subplot(1, 1, 1)\n",
        "    ctx = sp.matshow(cm)\n",
        "    plt.xticks(list(range(0, 6)), labels=classes)\n",
        "    plt.yticks(list(range(0, 6)), labels=classes)\n",
        "    plt.colorbar(ctx)\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "print('Using TensorFlow version', tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow version 2.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JsBpezExIga"
      },
      "source": [
        "##  Importing Data\n",
        "\n",
        "1. Importing the Tweet Emotion dataset\n",
        "2. Creating train, validation and test sets\n",
        "3. Extracting tweets and labels from the examples"
      ]
    },
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        "outputId": "b5918cfb-1413-4c5e-cdc1-d80d5c84530d"
      },
      "source": [
        "dataset= nlp.load_dataset('emotion')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/3.41k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset emotion/default (download: 1.97 MiB, generated: 2.09 MiB, post-processed: Unknown sizetotal: 4.06 MiB) to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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        {
          "output_type": "display_data",
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        {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
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              "0 examples [00:00, ? examples/s]"
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        {
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              "0 examples [00:00, ? examples/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s0h541FxIgc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "182e5b3b-16e7-444e-f438-b00bce8de9aa"
      },
      "source": [
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 2000),\n",
              " 'train': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 16000),\n",
              " 'validation': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 2000)}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7eCnxU25eiN"
      },
      "source": [
        "train= dataset['train']\n",
        "val= dataset['validation']\n",
        "test = dataset['test']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDYXMfZy5eiP"
      },
      "source": [
        "def get_tweet(data):\n",
        "  tweets= [x['text'] for x in data]\n",
        "  labels = [x['label']for x in data]\n",
        "  return tweets, labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeq3-vSB5eiR"
      },
      "source": [
        "tweets, labels =  get_tweet (train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHD3Tk0J5eiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "045dd64e-d873-43f1-d5fb-e150b5a0249f"
      },
      "source": [
        "tweets[1], labels[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n",
              " 'sadness')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcAflLv6xIgp"
      },
      "source": [
        "## Tokenizer\n",
        "\n",
        "1. Tokenizing the tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfX5-ResxIgq"
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cckUvwBo5eif"
      },
      "source": [
        "tokenizer =Tokenizer(num_words=10000, oov_token='<UNK>')\n",
        "tokenizer.fit_on_texts(tweets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.texts_to_sequences([tweets[1]])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyAQg8yaAWpS",
        "outputId": "c60da24d-9f64-4e1f-f7a2-96dad002d62a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2,\n",
              "  40,\n",
              "  101,\n",
              "  60,\n",
              "  8,\n",
              "  15,\n",
              "  494,\n",
              "  5,\n",
              "  15,\n",
              "  3496,\n",
              "  553,\n",
              "  32,\n",
              "  60,\n",
              "  61,\n",
              "  128,\n",
              "  148,\n",
              "  76,\n",
              "  1480,\n",
              "  4,\n",
              "  22,\n",
              "  1255]]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "UEvl3s0JAlFV",
        "outputId": "653d25d2-0282-4c19-d6b2-6e103edf3362"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3Bqm7b2xIgu"
      },
      "source": [
        "## Padding and Truncating Sequences\n",
        "\n",
        "1. Checking length of the tweets\n",
        "2. Creating padded sequences"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lengths =[len(t.split(' ')) for t in tweets ]\n",
        "plt.hist(lengths, bins= len(set(lengths)))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "aPXpkzORASca",
        "outputId": "b4552ed5-aa85-4b7b-ae57-9a87b5112c6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLvf_WFZxIgu"
      },
      "source": [
        "maxlen = 50\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOi5lIE3xIgx"
      },
      "source": [
        "def get_sequences(tokenizer, tweets):\n",
        "  sequences = tokenizer.texts_to_sequences(tweets)\n",
        "  padded = pad_sequences(sequences, truncating='post', padding='post', maxlen=maxlen)\n",
        "  return padded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9J_Iemf5eiq"
      },
      "source": [
        "padded_train_seq= get_sequences(tokenizer,tweets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eglH77ky5ei0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea08d64e-4f62-4df2-c6a0-c9ab5491fddc"
      },
      "source": [
        "padded_train_seq[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  2, 139,   3, 679,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         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,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGR473HA5ei7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BURhOX_KxIg8"
      },
      "source": [
        "## Preparing the Labels\n",
        "\n",
        "1. Creating classes to index and index to classes dictionaries\n",
        "2. Converting text labels to numeric labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SufT2bpD5ejE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "621bef22-abf0-4f2c-992d-c7d18e07557e"
      },
      "source": [
        "classes =set(labels)\n",
        "print(classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fear', 'anger', 'love', 'joy', 'sadness', 'surprise'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwzL88I7YSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "1c276c85-7cec-4326-9ac4-c41b154d6cee"
      },
      "source": [
        "plt.hist(labels,bins=11)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATwElEQVR4nO3df7SlVX3f8fdHBgWxZfgxYdGBemmcxmK6VJjyoyapAR0Q0kIrKqxER0I7NQuT2HY1GZssMSopxqySulptSZjlgEbE+AMWEHEWiqYm/BhAfocwRSjM4sfIAA0laMBv/3j21cN479x7Z+49d4b9fq1113me/ezzPHuf85zP2WefHzdVhSSpDy9Z7AZIksbH0Jekjhj6ktQRQ1+SOmLoS1JHlix2A7bnwAMPrImJicVuhiTtVm666abvVtWyqbbt0qE/MTHBxo0bF7sZkrRbSfLAdNuc3pGkjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI7s0t/IlaRdxcTaK8d6vPvPO3lB9utIX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHZhX6Se5PcnuSbyfZ2Mr2T7Ihyb3tcr9WniQfT7IpyW1JjhjZz+pW/94kqxemS5Kk6cxlpP/zVfW6qlrZ1tcC11TVCuCatg7wFmBF+1sDfBKGJwngHOBo4CjgnMknCknSeOzM9M4pwPq2vB44daT8ohpcByxNcjBwArChqrZW1RPABuDEnTi+JGmOZhv6BXw1yU1J1rSyg6rq4bb8CHBQW14OPDhy3Yda2XTlL5BkTZKNSTZu2bJlls2TJM3GbH9a+WeqanOSnwA2JPnL0Y1VVUlqPhpUVRcAFwCsXLlyXvYpSRrMaqRfVZvb5WPAlxjm5B9t0za0y8da9c3AoSNXP6SVTVcuSRqTGUM/yT5J/s7kMrAKuAO4HJj8BM5q4LK2fDnwrvYpnmOAp9o00NXAqiT7tTdwV7UySdKYzGZ65yDgS0km6/9xVX0lyY3ApUnOAh4A3t7qXwWcBGwCngHOBKiqrUk+DNzY6n2oqrbOW08kSTOaMfSr6j7gtVOUPw4cP0V5AWdPs691wLq5N1OSNB/8Rq4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR2Z7W/v7JYm1l45tmPdf97JYzuWJO0oR/qS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHZl16CfZI8ktSa5o64cluT7JpiSfS/LSVv6ytr6pbZ8Y2cf7W/k9SU6Y785IkrZvLiP9XwfuHln/KHB+Vb0KeAI4q5WfBTzRys9v9UhyOHA68BrgROATSfbYueZLkuZiVqGf5BDgZOCP2nqA44A/aVXWA6e25VPaOm378a3+KcAlVfW9qvoOsAk4aj46IUmandmO9P8A+A3gB239AODJqnqurT8ELG/Ly4EHAdr2p1r9H5ZPcZ0fSrImycYkG7ds2TKHrkiSZjJj6Cf5BeCxqrppDO2hqi6oqpVVtXLZsmXjOKQkdWPJLOq8AfgXSU4C9gL+LvBfgaVJlrTR/CHA5lZ/M3Ao8FCSJcC+wOMj5ZNGryNJGoMZR/pV9f6qOqSqJhjeiP1aVf0i8HXgtFZtNXBZW768rdO2f62qqpWf3j7dcxiwArhh3noiSZrRbEb60/lN4JIkHwFuAS5s5RcCFyfZBGxleKKgqu5McilwF/AccHZVPb8Tx5ckzdGcQr+qrgWubcv3McWnb6rqWeBt01z/XODcuTZSkjQ//EauJHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6siMoZ9kryQ3JLk1yZ1JfqeVH5bk+iSbknwuyUtb+cva+qa2fWJkX+9v5fckOWGhOiVJmtpsRvrfA46rqtcCrwNOTHIM8FHg/Kp6FfAEcFarfxbwRCs/v9UjyeHA6cBrgBOBTyTZYz47I0navhlDvwZPt9U9218BxwF/0srXA6e25VPaOm378UnSyi+pqu9V1XeATcBR89ILSdKszGpOP8keSb4NPAZsAP438GRVPdeqPAQsb8vLgQcB2vangANGy6e4zuix1iTZmGTjli1b5t4jSdK0ZhX6VfV8Vb0OOIRhdP7qhWpQVV1QVSurauWyZcsW6jCS1KU5fXqnqp4Evg4cCyxNsqRtOgTY3JY3A4cCtO37Ao+Plk9xHUnSGMzm0zvLkixty3sDbwbuZgj/01q11cBlbfnytk7b/rWqqlZ+evt0z2HACuCG+eqIJGlmS2auwsHA+vZJm5cAl1bVFUnuAi5J8hHgFuDCVv9C4OIkm4CtDJ/YoaruTHIpcBfwHHB2VT0/v92RJG3PjKFfVbcBr5+i/D6m+PRNVT0LvG2afZ0LnDv3ZkqS5oPfyJWkjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktSRJYvdAO2YibVXjvV495938liPJ2lhONKXpI4Y+pLUkRlDP8mhSb6e5K4kdyb59Va+f5INSe5tl/u18iT5eJJNSW5LcsTIvla3+vcmWb1w3ZIkTWU2I/3ngP9QVYcDxwBnJzkcWAtcU1UrgGvaOsBbgBXtbw3wSRieJIBzgKOBo4BzJp8oJEnjMWPoV9XDVXVzW/5r4G5gOXAKsL5VWw+c2pZPAS6qwXXA0iQHAycAG6pqa1U9AWwATpzX3kiStmtOc/pJJoDXA9cDB1XVw23TI8BBbXk58ODI1R5qZdOVS5LGZNahn+QVwBeA91XV/x3dVlUF1Hw0KMmaJBuTbNyyZct87FKS1Mwq9JPsyRD4n6mqL7biR9u0De3ysVa+GTh05OqHtLLpyl+gqi6oqpVVtXLZsmVz6YskaQYzfjkrSYALgbur6r+MbLocWA2c1y4vGyl/b5JLGN60faqqHk5yNfC7I2/ergLePz/dkLQr8EuDu77ZfCP3DcA7gduTfLuV/SeGsL80yVnAA8Db27argJOATcAzwJkAVbU1yYeBG1u9D1XV1nnphSRpVmYM/ar6X0Cm2Xz8FPULOHuafa0D1s2lgZKk+eM3ciWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6smSxGyBta2LtlWM93v3nnTzW40mLyZG+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSMzhn6SdUkeS3LHSNn+STYkubdd7tfKk+TjSTYluS3JESPXWd3q35tk9cJ0R5K0PbMZ6X8KOHGbsrXANVW1ArimrQO8BVjR/tYAn4ThSQI4BzgaOAo4Z/KJQpI0PjOGflV9E9i6TfEpwPq2vB44daT8ohpcByxNcjBwArChqrZW1RPABn78iUSStMB2dE7/oKp6uC0/AhzUlpcDD47Ue6iVTVf+Y5KsSbIxycYtW7bsYPMkSVPZ6Tdyq6qAmoe2TO7vgqpaWVUrly1bNl+7lSSx46H/aJu2oV0+1so3A4eO1DuklU1XLkkaox0N/cuByU/grAYuGyl/V/sUzzHAU20a6GpgVZL92hu4q1qZJGmMZvw9/SSfBd4IHJjkIYZP4ZwHXJrkLOAB4O2t+lXAScAm4BngTICq2prkw8CNrd6HqmrbN4clSQtsxtCvqjOm2XT8FHULOHua/awD1s2pdZKkeeU3ciWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI7M+Ns7kubXxNorx3as+887eWzH0u7Bkb4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI6MPfSTnJjkniSbkqwd9/ElqWdjDf0kewD/HXgLcDhwRpLDx9kGSerZuEf6RwGbquq+qvo+cAlwypjbIEndSlWN72DJacCJVfWv2/o7gaOr6r0jddYAa9rqTwH37MQhDwS+uxPX39301l+wz72wz3PzyqpaNtWGJTvenoVRVRcAF8zHvpJsrKqV87Gv3UFv/QX73Av7PH/GPb2zGTh0ZP2QViZJGoNxh/6NwIokhyV5KXA6cPmY2yBJ3Rrr9E5VPZfkvcDVwB7Auqq6cwEPOS/TRLuR3voL9rkX9nmejPWNXEnS4vIbuZLUEUNfkjryogj9JBNJ7ljsdmh+JXl6sduwu0lyVZKli92OuUjya0nuTvKZxW7LYkjy52M93othTj/JBHBFVf30Ijdlt5MkDOfBDxa7LdtK8nRVvWKx27GYkiypqudmUW+XvR9nkuQvgTdV1UM7sY9Z3U7axUb6SfZJcmWSW5PckeQdST6Q5Ma2fkE7uUlyZKt3K3D2yD7eneSLSb6S5N4kvzeybVWSv0hyc5LPJ3lFKz8vyV1Jbkvy+63sbe2Ytyb55phvCpJ8OclNSe5s31ImydNJzm1tui7JQa38J9v67Uk+MjpCTvIf2+13W5LfaWUT7UfvLgLu4IXfndjlZPCxdn/cnuQdrfySJCeP1PtUktOS7NHqT/b73y5e63/YtqnO7fuTHNi2r0xybVv+YJKLk3wLuLid05clubad0+e0ej92P07uc6rjtescmeQb7dy6OsnBi3OLDJL8D+AfAH+a5LeSrEtyQ5JbkpzS6kwk+bP2uL05yT9t5W9s5ZcDdy1iN3ZKe1xPd45flOTUkbqfmbxddlhV7TJ/wFuBPxxZ3xfYf2T9YuCft+XbgJ9ryx8D7mjL7wbua9fdC3iAIdQOBL4J7NPq/SbwAeAAhp96mHzVs7Rd3g4sHy0b822xf7vcm+EBfQBQI/3/PeC32/IVwBlt+T3A0215FcPHvsLwBH8F8HPABPAD4JjFvs9nuA0m+/FWYAPDx3wPAv4PcDDwL4H1rc5LgQfb7bVm5LZ5GbAROGwXPLfvBw5s6yuBa9vyB4GbgL1HzumH2zkweT6snOp+nNznNMfbE/hzYFkrewfDx6YX+36ebPPvAr/UypYCfwXsA7wc2KuVrwA2tuU3Av9vse/b+TjPt3OO/zPgyyP34XeAJTtzvF1qpM8QtG9O8tEkP1tVTwE/n+T6JLcDxwGvyTBnubSqJkfgF2+zn2uq6qmqepZhBPBK4BiGX/b8VpJvA6tb+VPAs8CFSf4V8Ezbx7eATyX5Nwx3xLj9WnsVcx3Dk9YK4PsMwQ1DKEy05WOBz7flPx7Zx6r2dwtwM/Dqth+AB6rquoVq/Dz7GeCzVfV8VT0KfAP4J8CfMpwfL2P45dZvVtXfMPT5Xe1+vp4hLFdMveuxmerc3p7LW18mbaiqx1vZFxluE5j+fpzqeD8F/DSwod02v83wrfhdxSpgbWvbtQyDtr/P8GT1hy0DPs/wOJ50Q1V9Z9wNXQBTnuNV9Q2GL7QuA84AvlA7OY21S/32TlX9VZIjgJOAjyS5hmHqZmVVPZjkgwwnwky+N7L8PEM/w/DAOWPbykmOAo4HTgPeCxxXVe9JcjRwMnBTkiOr6vGd6N6sJXkj8Cbg2Kp6pr3s3wv422pP+fyoX9vdFfCfq+p/brP/CYYR0m6tqp5tt80JDKPWS9qmAL9aVVcvVtu2Nc25/Rw/mmLd9rze9v7Z9s23mqbe9o73JeDOqjp2B7ux0AK8tape8COL7XH/KPBahtvr2ZHNu/15PAsXAb/E8AsGZ+7sznapkX6Svwc8U1WfZpiyOaJt+m6G+ffTAKrqSeDJJJOjnV+cxe6vA96Q5FXtWPsk+Ydtv/tW1VXAv2M4sUjyk1V1fVV9ANjCeOe99wWeaIH/aoZXKdtzHcPLQxhOjElXA7+cH713sTzJT8x7axfenwHvaHP1yximqG5o2z7H8ED4WeArrexq4FeS7AnQ7ud9xtzmF5jm3L4fOLJVees0V5305iT7J9kbOJXhlehcj3cPsCzJsa3Onkles4NdWghXA7+a/PB9u9e38n2Bh2t4k/qdLM4r74W2vXP8U8D7AKpqp9+72KVG+sA/Bj6W5AfA3wK/wnCC3wE8wvDbPZPOBNYlKeCrM+24qrYkeTfw2TYdAMPL278GLkuyF8NI49+3bR9LsqKVXQPcupN9m4uvAO9JcjfDA3WmaZj3AZ9O8lvtuk8BVNVXk/wj4C/a4+hphhHD8wvV8AXyJYYprFsZRri/UVWPtG1fZZjeu6yG/9EA8EcMU183twDZwnAeLaapzu29GaYVP8wwnbE9NwBfYJiO+XRVbWyv2GZ9vKr6foafN/94kn0ZHv9/ACzkT6HMxYcZ2nNbkpcwzF//AvAJ4AtJ3sVwfr/YRvfFds7xqnq0ZcGX5+NgL4qPbPYuycuBv6mqSnI6w5u6/nOaF4k2WFlZI/93Qi8OSQ4Abq6qV26nzssZ3qM5YhbvBc1oVxvpa8ccCfy3Nqp9EvjlRW6PpBm0Kbhrgd/fTp03ARcC589H4IMjfUnqyi71Rq4kaWEZ+pLUEUNfkjpi6EtSRwx9SerI/wcBMHv+AoKmlwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNLF6rXL5ejN"
      },
      "source": [
        "class_to_index = dict((c,i) for i,c in enumerate(classes))\n",
        "index_to_class = dict((v,k) for k,v in class_to_index.items())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08InVyM5ejc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3db1b14d-855b-462e-9814-1d88bae832ca"
      },
      "source": [
        "class_to_index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'anger': 1, 'fear': 0, 'joy': 3, 'love': 2, 'sadness': 4, 'surprise': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpeDoA6gxIhE"
      },
      "source": [
        "names_to_ids =lambda labels: np.array([class_to_index.get(x) for x in labels])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq0WJYsP5ejR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4ec126b-7fc5-4094-b63d-fdd914971bca"
      },
      "source": [
        "train_labels = names_to_ids(labels)\n",
        "print(train_labels[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-v0Mnh8xIhP"
      },
      "source": [
        "## Creating the Model\n",
        "\n",
        "1. Creating the model\n",
        "2. Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpewXxPQ5eji"
      },
      "source": [
        " model = tf.keras.models.Sequential([\n",
        "         tf.keras.layers.Embedding(10000, 16, input_length=maxlen),\n",
        "         tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20,return_sequences=True)),\n",
        "         tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20)),\n",
        "         tf.keras.layers.Dense(6, activation='softmax')\n",
        " ])\n",
        "\n",
        " model.compile(\n",
        "     \n",
        "     loss ='sparse_categorical_crossentropy',\n",
        "     optimizer='adam',\n",
        "     metrics=['accuracy']\n",
        " )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-A8FIjNJH_v",
        "outputId": "4f73e0d5-e105-4fa4-887d-ba2589ae9a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 50, 16)            160000    \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirectio  (None, 50, 40)           5920      \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " bidirectional_3 (Bidirectio  (None, 40)               9760      \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 246       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 175,926\n",
            "Trainable params: 175,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HST_CHjxIhR"
      },
      "source": [
        "##  Training the Model\n",
        "\n",
        "1. Preparing a validation set\n",
        "2. Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff7F3hCK5ejm"
      },
      "source": [
        "val_tweets, val_labels  = get_tweet(val)\n",
        "val_seq = get_sequences(tokenizer, val_tweets)\n",
        "val_labels =names_to_ids(val_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlMKaZ3H5ejr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4920c8d-dfe1-4a25-c1b2-04b9d9760ee6"
      },
      "source": [
        "val_tweets[0], val_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('im feeling quite sad and sorry for myself but ill snap out of it soon', 4)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzBqnWQ-5ejw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f98ce35-c3ed-4745-e3ac-4cdcc6e4edf2"
      },
      "source": [
        "h = model.fit(\n",
        "    padded_train_seq, train_labels,\n",
        "    validation_data=(val_seq, val_labels),\n",
        "    epochs=20,\n",
        "    callbacks=[\n",
        "               tf.keras.callbacks.EarlyStopping(monitor = 'val_accuracy', patience=2)\n",
        "    ]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "500/500 [==============================] - 18s 16ms/step - loss: 1.2991 - accuracy: 0.4985 - val_loss: 1.0141 - val_accuracy: 0.6070\n",
            "Epoch 2/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.7901 - accuracy: 0.7105 - val_loss: 0.7722 - val_accuracy: 0.7240\n",
            "Epoch 3/20\n",
            "500/500 [==============================] - 7s 15ms/step - loss: 0.5135 - accuracy: 0.8237 - val_loss: 0.6138 - val_accuracy: 0.8045\n",
            "Epoch 4/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.3279 - accuracy: 0.8884 - val_loss: 0.5401 - val_accuracy: 0.8375\n",
            "Epoch 5/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.2281 - accuracy: 0.9259 - val_loss: 0.4991 - val_accuracy: 0.8485\n",
            "Epoch 6/20\n",
            "500/500 [==============================] - 8s 16ms/step - loss: 0.1713 - accuracy: 0.9449 - val_loss: 0.4512 - val_accuracy: 0.8650\n",
            "Epoch 7/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1271 - accuracy: 0.9595 - val_loss: 0.4376 - val_accuracy: 0.8600\n",
            "Epoch 8/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1033 - accuracy: 0.9663 - val_loss: 0.4497 - val_accuracy: 0.8775\n",
            "Epoch 9/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0885 - accuracy: 0.9700 - val_loss: 0.4491 - val_accuracy: 0.8825\n",
            "Epoch 10/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0712 - accuracy: 0.9769 - val_loss: 0.4299 - val_accuracy: 0.8765\n",
            "Epoch 11/20\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0606 - accuracy: 0.9794 - val_loss: 0.4531 - val_accuracy: 0.8810\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdsJyMTLxIhX"
      },
      "source": [
        "## Evaluating the Model\n",
        "\n",
        "1. Visualizing training history\n",
        "2. Prepraring a test set\n",
        "3. A look at individual predictions on the test set\n",
        "4. A look at all predictions on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENCfvXeLxIhX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "e213babb-e450-42cb-a07a-6e91b54148b1"
      },
      "source": [
        "show_history(h)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWuzoz8uxIha"
      },
      "source": [
        "test_tweets, test_labels =get_tweet(test)\n",
        "test_seq = get_sequences(tokenizer, test_tweets)\n",
        "test_labels= names_to_ids(test_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vRVJ_2SxIhc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcfac664-b08a-4a96-caa2-d5faccdddd9d"
      },
      "source": [
        "_= model.evaluate (test_seq, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 7ms/step - loss: 0.4598 - accuracy: 0.8775\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh638vHG5ej6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32fdd5e0-03c6-4681-8b75-8697daec0793"
      },
      "source": [
        "i= random.randint(0, len(test_labels)-1)\n",
        "\n",
        "print('Sentence:' , test_tweets[i])\n",
        "print('Emotion:', index_to_class[test_labels[i]])\n",
        "\n",
        "p = model.predict(np.expand_dims(test_seq[i], axis=0))[0]\n",
        "pred_class = index_to_class[np.argmax(p).astype('uint8')]\n",
        "\n",
        "print('Predicted Emotion', pred_class)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence: i feel at leaving work is hot and complicated and tempered with the disquiet of a future that feels out of my hands\n",
            "Emotion: love\n",
            "Predicted Emotion love\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHl5SVCFxIhh"
      },
      "source": [
        "preds =  np.argmax(model.predict(test_seq), axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC8YQ0OexIhj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "49d6d18b-f66b-48df-9711-e4c59ca65699"
      },
      "source": [
        "show_confusion_matrix(test_labels, preds, list(classes))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}