Twitter Emotion Recognition using RNN.ipynb

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  "cells": [
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      },
      "source": [
        "## **Twitter Emotion Recognition using RNN**\n",
        "\n",
        "---\n",
        "\n",
        "[Tweet Emotion Dataset](https://github.com/dair-ai/emotion_dataset)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLnDPdYJiJHv"
      },
      "source": [
        "## 1. Installing Hugging Face's nlp package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "5agZRy-45i0g",
        "outputId": "0e480392-362c-48b4-ec49-d2cdda488d61"
      },
      "source": [
        "!pip install nlp"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting nlp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/09/e3/bcdc59f3434b224040c1047769c47b82705feca2b89ebbc28311e3764782/nlp-0.4.0-py3-none-any.whl (1.7MB)\n",
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            "Collecting xxhash\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e7/27/1c0b37c53a7852f1c190ba5039404d27b3ae96a55f48203a74259f8213c9/xxhash-2.0.0-cp37-cp37m-manylinux2010_x86_64.whl (243kB)\n",
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            "Installing collected packages: xxhash, nlp\n",
            "Successfully installed nlp-0.4.0 xxhash-2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ecz7shv-ClA1"
      },
      "source": [
        "## 2. Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKFjWz6e5eiH"
      },
      "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"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "826tQBp5zlb1"
      },
      "source": [
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvfVk2GNiJH5"
      },
      "source": [
        "## 3. Importing the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YHOvjAu5eiL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342,
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        },
        "outputId": "411eb4c0-7fd8-4c15-f388-85a530564000"
      },
      "source": [
        "dataset = nlp.load_dataset('emotion')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34ce67545af640338c2e45faf2650280",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3406.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30dd749c33a64eef957e3a3707a452d3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=6011.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1af1d8a7c664c148cea0182a04ccde9",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1658616.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "643a82a9cd1647c7bec72b193f962918",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=204240.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e75566c54bd64f03835d2c4a01308ead",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=206760.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a3ee52a06b04007adf3f6316146bb57",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "cedd341a7bf34e9ab6f5635e739d6bb6",
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        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f4168ede76c845c7a5106fde9eaec5da",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpi1dL1wiJH8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "092914bf-923f-4bb4-86c8-ca295b0b851b"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 5,
      "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": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7eCnxU25eiN"
      },
      "source": [
        "train = dataset['train']\n",
        "val = dataset['validation']\n",
        "test = dataset['test']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDYXMfZy5eiP"
      },
      "source": [
        "def get_tweets(data):\n",
        "    tweets = [x['text'] for x in data]\n",
        "    labels = [x['label'] for x in data]\n",
        "    return tweets, labels"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeq3-vSB5eiR"
      },
      "source": [
        "tweets, labels = get_tweets(train)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHD3Tk0J5eiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dde40bf6-e662-49d9-da29-be09514ea4b2"
      },
      "source": [
        "tweets[10], labels[10]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i feel like i have to make the suffering i m seeing mean something',\n",
              " 'sadness')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9guH0-5qiJII"
      },
      "source": [
        "## 4. Tokenizing the Tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYN8LWDmiJII"
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cckUvwBo5eif",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a8e8d73-52a1-4789-ee67-8c64fa7334ed"
      },
      "source": [
        "tokenizer = Tokenizer(num_words=10000, oov_token='<UNK>')\n",
        "\n",
        "tokenizer.fit_on_texts(tweets)\n",
        "\n",
        "print(tokenizer.texts_to_sequences([tweets[10]]))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[2, 3, 14, 2, 21, 5, 80, 6, 733, 2, 93, 544, 304, 84]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AwQt4AgiJIJ"
      },
      "source": [
        "## 5. Padding and Truncating Sequences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbDdupDkiJIJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "046592bd-a3a1-4b9b-e3a8-5789610c84d1"
      },
      "source": [
        "lengths = [len(t.split(' ')) for t in tweets]\n",
        "\n",
        "plt.hist(lengths, bins=len(set(lengths)))\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpOTvhoriJIK"
      },
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9J_Iemf5eiq"
      },
      "source": [
        "def get_sequences(tokenizer, tweets):\n",
        "    sequences = tokenizer.texts_to_sequences(tweets)\n",
        "    padded_sequences = pad_sequences(sequences, truncating='post', maxlen=50, padding='post')\n",
        "    return padded_sequences"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eglH77ky5ei0"
      },
      "source": [
        "padded_train_sequences = get_sequences(tokenizer, tweets)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGR473HA5ei7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24268d40-1685-4b2d-bc6b-dd5f2f6dbaf5"
      },
      "source": [
        "padded_train_sequences[10]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  2,   3,  14,   2,  21,   5,  80,   6, 733,   2,  93, 544, 304,\n",
              "        84,   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": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZ5SSsM4iJIL"
      },
      "source": [
        "## 6. Preparing the Labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SufT2bpD5ejE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "985137b7-b4bb-432e-99e9-186545507243"
      },
      "source": [
        "classes = set(labels)\n",
        "print(classes)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'surprise', 'joy', 'love', 'anger', 'fear', 'sadness'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwzL88I7YSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "d1696bc7-48d5-4e3d-afc9-96cc6437bddd"
      },
      "source": [
        "plt.hist(labels, bins=11)\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNLF6rXL5ejN"
      },
      "source": [
        "classes_to_index = dict((c, i) for i, c in enumerate(classes))\n",
        "index_to_classes = dict((v, k) for k, v in classes_to_index.items())"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08InVyM5ejc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb09b1d1-b856-4daa-c6b3-b1891f240bad"
      },
      "source": [
        "classes_to_index"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'anger': 3, 'fear': 4, 'joy': 1, 'love': 2, 'sadness': 5, 'surprise': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJx0hFItiJIO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc92c81e-26c9-44a8-d3df-5087c0ad28e1"
      },
      "source": [
        "index_to_classes"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'surprise', 1: 'joy', 2: 'love', 3: 'anger', 4: 'fear', 5: 'sadness'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq0WJYsP5ejR"
      },
      "source": [
        "names_to_ids = lambda labels: np.array([classes_to_index.get(x) for x in labels])"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v15KnrNC5ejW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59f47aaf-a740-40d6-a360-696b9a34de43"
      },
      "source": [
        "train_labels = names_to_ids(labels)\n",
        "print(train_labels[10])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bD4np-KsiJIS"
      },
      "source": [
        "## 7. Creating the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpewXxPQ5eji",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5c14cc7-90de-4934-9826-7787fc294cb8"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Embedding(10000, 16, input_length=50),\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",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 50, 16)            160000    \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 50, 40)            5920      \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 40)                9760      \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 246       \n",
            "=================================================================\n",
            "Total params: 175,926\n",
            "Trainable params: 175,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiU-m6wEiJIT"
      },
      "source": [
        "## 8. Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff7F3hCK5ejm"
      },
      "source": [
        "val_tweets, val_labels = get_tweets(val)\n",
        "val_sequences = get_sequences(tokenizer, val_tweets)\n",
        "val_labels = names_to_ids(val_labels)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlMKaZ3H5ejr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0312fa07-8257-430a-a919-df232ca5a08e"
      },
      "source": [
        "val_tweets[20], val_labels[20]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i just know to begin with i am going to feel shy about it', 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzBqnWQ-5ejw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bc22944-c411-402c-f760-285364fa581e"
      },
      "source": [
        "h = model.fit(\n",
        "    padded_train_sequences, train_labels,\n",
        "    validation_data=(val_sequences, val_labels),\n",
        "    epochs=15,\n",
        "    callbacks=[\n",
        "        tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2)\n",
        "    ]\n",
        ")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "500/500 [==============================] - 43s 16ms/step - loss: 1.5259 - accuracy: 0.3709 - val_loss: 0.9029 - val_accuracy: 0.6750\n",
            "Epoch 2/15\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.6728 - accuracy: 0.7521 - val_loss: 0.5371 - val_accuracy: 0.8195\n",
            "Epoch 3/15\n",
            "500/500 [==============================] - 7s 13ms/step - loss: 0.3325 - accuracy: 0.8875 - val_loss: 0.5089 - val_accuracy: 0.8340\n",
            "Epoch 4/15\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.2216 - accuracy: 0.9301 - val_loss: 0.4228 - val_accuracy: 0.8725\n",
            "Epoch 5/15\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1579 - accuracy: 0.9494 - val_loss: 0.4390 - val_accuracy: 0.8740\n",
            "Epoch 6/15\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.1180 - accuracy: 0.9630 - val_loss: 0.4339 - val_accuracy: 0.8820\n",
            "Epoch 7/15\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0989 - accuracy: 0.9676 - val_loss: 0.3980 - val_accuracy: 0.8820\n",
            "Epoch 8/15\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0789 - accuracy: 0.9757 - val_loss: 0.3820 - val_accuracy: 0.8975\n",
            "Epoch 9/15\n",
            "500/500 [==============================] - 7s 14ms/step - loss: 0.0640 - accuracy: 0.9790 - val_loss: 0.4468 - val_accuracy: 0.8915\n",
            "Epoch 10/15\n",
            "500/500 [==============================] - 7s 13ms/step - loss: 0.0538 - accuracy: 0.9850 - val_loss: 0.4302 - val_accuracy: 0.8935\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiKaj-3RiJIV"
      },
      "source": [
        "## 9. Evaluating the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbI05hwKiJIV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "a4cdfca3-e0f4-427b-ff7a-03e91659c496"
      },
      "source": [
        "def show_history(h):\r\n",
        "    epochs_trained = len(h.history['loss'])\r\n",
        "    plt.figure(figsize=(16, 6))\r\n",
        "\r\n",
        "    plt.subplot(1, 2, 1)\r\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('accuracy'), label='Training')\r\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_accuracy'), label='Validation')\r\n",
        "    plt.ylim([0., 1.])\r\n",
        "    plt.xlabel('Epochs')\r\n",
        "    plt.ylabel('Accuracy')\r\n",
        "    plt.legend()\r\n",
        "\r\n",
        "    plt.subplot(1, 2, 2)\r\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('loss'), label='Training')\r\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_loss'), label='Validation')\r\n",
        "    plt.xlabel('Epochs')\r\n",
        "    plt.ylabel('Loss')\r\n",
        "    plt.legend()\r\n",
        "    plt.show()\r\n",
        "\r\n",
        "show_history(h)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xCKeBP1iJIV"
      },
      "source": [
        "test_tweets, test_labels = get_tweets(test)\n",
        "test_sequences = get_sequences(tokenizer, test_tweets)\n",
        "test_labels = names_to_ids(test_labels)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aM7kditpiJIV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74835320-d476-4916-8231-d6a92f5501d4"
      },
      "source": [
        "eval = model.evaluate(test_sequences, test_labels)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 [==============================] - 0s 6ms/step - loss: 0.4190 - accuracy: 0.8880\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh638vHG5ej6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa8f053a-58c8-4175-d517-e7121080ab69"
      },
      "source": [
        "for a in range(0,5):\n",
        "  i = random.randint(0, len(test_labels) - 1)\n",
        "\n",
        "  print('Tweet:', test_tweets[i])\n",
        "  print('Actual Emotion:', index_to_classes[test_labels[i]])\n",
        "\n",
        "  p = model.predict_classes(np.expand_dims(test_sequences[i], axis=0))[0]\n",
        "\n",
        "  print('Predicted Emotion:', index_to_classes.get(p), '\\n')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tweet: i woke up and felt sad all over again but that was quickly replaced with a feeling that reassured me things will work themselves out on their own time\n",
            "Actual Emotion: joy\n",
            "Predicted Emotion: joy \n",
            "\n",
            "Tweet: i feel really irritated when i talk about my problems and people start talking about theirs\n",
            "Actual Emotion: anger\n",
            "Predicted Emotion: anger \n",
            "\n",
            "Tweet: i feel crappy i eat crappy\n",
            "Actual Emotion: sadness\n",
            "Predicted Emotion: sadness \n",
            "\n",
            "Tweet: i am feeling overwhelmed by trying to do it all that i think on the women before me\n",
            "Actual Emotion: surprise\n",
            "Predicted Emotion: fear \n",
            "\n",
            "Tweet: i finally arrived home a couple of hours later feeling somewhat exhausted dehydrated and even sun burnt\n",
            "Actual Emotion: sadness\n",
            "Predicted Emotion: sadness \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_i9LUUDsiJIW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3272c03-82b6-4a1a-95a0-0ff17382ae75"
      },
      "source": [
        "preds = model.predict_classes(test_sequences)\n",
        "preds.shape, test_labels.shape"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2000,), (2000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nLW-iSYiJIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "4ea77330-8ef5-4e76-cac9-494e4bf21663"
      },
      "source": [
        "def show_confusion_matrix(y_true, y_pred, classes):\r\n",
        "    from sklearn.metrics import confusion_matrix\r\n",
        "    \r\n",
        "    cm = confusion_matrix(y_true, y_pred, normalize='true')\r\n",
        "\r\n",
        "    plt.figure(figsize=(8, 8))\r\n",
        "    sp = plt.subplot(1, 1, 1)\r\n",
        "    ctx = sp.matshow(cm)\r\n",
        "    plt.xticks(list(range(0, 6)), labels=classes)\r\n",
        "    plt.yticks(list(range(0, 6)), labels=classes)\r\n",
        "    plt.colorbar(ctx)\r\n",
        "    plt.show()\r\n",
        "    \r\n",
        "show_confusion_matrix(test_labels, preds, list(classes))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}