sentiment_analysis_3_deep_learning_models.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sentiment_analysis_3_deep_learning_models.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOS0uoMKbhn+Ko+TL3wQwJ4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CuGp-34ZPqI"
      },
      "source": [
        "# Sentiment Analysis with Deep Learning\n",
        "\n",
        "On various NLP tasks neural-based models achieve superior results compared to traditional machine learning models like SVM or logistic regression. Let's use prepared word vectors (BOW, TF-IDF and Word2Vec) to test their performance with neural networks.\n",
        "\n",
        "There will be used three different types of deep neural networks:\n",
        "* Densely Connected Neural Network\n",
        "* RNN - Recurrent Neural Network (with LSTM - Long Short Term Memory Network)\n",
        "* CNN - Convolutional Neural Network\n",
        "\n",
        "As a baseline, let's use the best result achieved so far: accuracy - 95,8% and f1-score - 0,9774 for Logistic Regression with TF-IDF word embeddings.\n",
        "\n",
        "\\* This notebook was created in Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "662YQkWCPNXL"
      },
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from google.colab import drive\n",
        "\n",
        "from keras import models, layers, metrics, regularizers"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9COZd9qvcxu",
        "outputId": "c3c2e6ba-3c59-46f0-cf1d-55887d02f507",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "drive.mount('/gdrive')\n",
        "%cd '/gdrive/My Drive/Colab Notebooks/nlp_toys_reviews'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n",
            "/gdrive/My Drive/Colab Notebooks/nlp_toys_reviews\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuyQ85UJnbao"
      },
      "source": [
        "def load_pickle(path):\n",
        "  with open(path, 'rb') as handle:\n",
        "    return pickle.load(handle)\n",
        "\n",
        "def f1_score(precision, recall):\n",
        "  return (2 * precision * recall) / (precision + recall)\n",
        "\n",
        "def unpack_history(history):\n",
        "  history_dict = {k: np.array(v) for k,v in history.history.items()}\n",
        "  history_dict['f1'] = f1_score(history_dict['precision'], history_dict['recall'])\n",
        "  history_dict['val_f1'] = f1_score(history_dict['val_precision'], history_dict['val_recall'])\n",
        "\n",
        "  epochs = range(1,len(history_dict['accuracy'])+1)\n",
        "\n",
        "  return history_dict, epochs\n",
        "\n",
        "def highest_score(metric, metric_name):\n",
        "  highest_score = [i for i, j in enumerate(metric) if j == max(metric)][0]\n",
        "  print('The highest %s score in %d epoch: %f' % (metric_name, highest_score+1, metric[highest_score]))\n",
        "\n",
        "def plot_acc_and_f1(accuracy_train, accuracy_val, f1_train, f1_val, epochs):\n",
        "  fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 5))\n",
        "\n",
        "  sns.scatterplot(x=epochs, y=accuracy_train, ax=ax1, label='train')\n",
        "  sns.lineplot(x=epochs, y=accuracy_val, ax=ax1, label='test')\n",
        "  ax1.set_title('Train and test accuracy')\n",
        "  ax1.set_ylabel('accuracy')\n",
        "  ax1.set_xlabel('epoch')\n",
        "\n",
        "  sns.scatterplot(x=epochs, y=f1_train, ax=ax2, label='train')\n",
        "  sns.lineplot(x=epochs, y=f1_val, ax=ax2, label='test')\n",
        "  ax2.set_title('Train and test F1 score')\n",
        "  ax2.set_ylabel('F1')\n",
        "  ax2.set_xlabel('epoch')\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "\n",
        "def model_report(history):\n",
        "  history_dict, epochs = unpack_history(history)\n",
        "\n",
        "  plot_acc_and_f1(history_dict['accuracy'], history_dict['val_accuracy'], history_dict['f1'], history_dict['val_f1'], epochs)\n",
        "\n",
        "  highest_score(history_dict['val_accuracy'], 'accuracy')\n",
        "  highest_score(history_dict['val_f1'], 'f1')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zt6OIYYkPalX"
      },
      "source": [
        "y = np.array(load_pickle('data/sentiment_100k.pickle'))\n",
        "metrics_definition = ['accuracy', metrics.Precision(), metrics.Recall()]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpSghnsBZAhV"
      },
      "source": [
        "## Densely Connected NN with Bag of Words Vectors\n",
        "\n",
        "This simple neural network with BOW word vectors gives slightly better results than the baseline. The optimal solution is to train the model for 5 epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mo-pRUIvWoI-"
      },
      "source": [
        "X = load_pickle('data/M_bow_100k.pickle').toarray()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYttLFsfV-Fg"
      },
      "source": [
        "model_bow = models.Sequential()\n",
        "model_bow.add(layers.Dense(16, activation='relu', input_shape=(X.shape[1],)))\n",
        "model_bow.add(layers.Dense(16, activation='relu'))\n",
        "model_bow.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model_bow.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=metrics_definition)\n",
        "\n",
        "history_bow = model_bow.fit(X, y,\n",
        "                            epochs=30,\n",
        "                            batch_size=256,\n",
        "                            validation_split=0.33,\n",
        "                            verbose=0)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GwoWzeiZMY-O",
        "outputId": "28b40b25-7f91-4af5-c73e-acab3e74b80e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "model_report(history_bow)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The highest accuracy score in 10 epoch: 0.958455\n",
            "The highest f1 score in 10 epoch: 0.977391\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uX0NPoxYaYI"
      },
      "source": [
        "## Densely Connected NN with TF-IDF Word Vectors\n",
        "\n",
        "Combination of simple neural network with TF-IDF word vectors doesn't show any improvement compared to the baseline. Model performance can be improved by eg changing number and size of layers but results still are at the similar level."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4Rf_0OZ21PB"
      },
      "source": [
        "X = load_pickle('data/M_tfidf_100k.pickle').toarray()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaGvDWgoPX51"
      },
      "source": [
        "model_tfidf = None\n",
        "\n",
        "model_tfidf = models.Sequential()\n",
        "model_tfidf.add(layers.Dense(32, activation='relu', input_shape=(X.shape[1],)))\n",
        "model_tfidf.add(layers.Dense(32, activation='relu'))\n",
        "model_tfidf.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model_tfidf.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=metrics_definition)\n",
        "\n",
        "history_tfidf = model_tfidf.fit(X, y,\n",
        "                                epochs=30,\n",
        "                                batch_size=512,\n",
        "                                validation_split=0.33,\n",
        "                                verbose=0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YoJmzy9PW7TR",
        "outputId": "7950fe27-4dad-4b39-d022-8ee92602d686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "model_report(history_tfidf)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The highest accuracy score in 9 epoch: 0.956061\n",
            "The highest f1 score in 8 epoch: 0.976117\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jMMZQwLY-jW"
      },
      "source": [
        "## Densely Connected NN with Word2Vec Embeddings\n",
        "\n",
        "It takes 16 epochs to achieve the best results with this simple neural network and Word2Vec word vectors. Compared to the baseline, there is no improvement."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntUM9yqZPeOi"
      },
      "source": [
        "X = load_pickle('data/M_word2vec_100k.pickle')"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebJo_fJqXMlm"
      },
      "source": [
        "model_w2v = None\n",
        "\n",
        "model_w2v = models.Sequential()\n",
        "model_w2v.add(layers.Dense(64, activation='relu', input_shape=(X.shape[1],)))\n",
        "model_w2v.add(layers.Dense(32, activation='relu'))\n",
        "model_w2v.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model_w2v.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=metrics_definition)\n",
        "\n",
        "history_w2v = model_w2v.fit(X, y,\n",
        "                            epochs=30,\n",
        "                            batch_size=256,\n",
        "                            validation_split=0.33,\n",
        "                            verbose=0)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjVPEOcyXMsz",
        "outputId": "ec92fcfa-7ef3-4a54-c4ba-853db9c064c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "model_report(history_w2v)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The highest accuracy score in 13 epoch: 0.954909\n",
            "The highest f1 score in 13 epoch: 0.975623\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvnZuqtBVoXn"
      },
      "source": [
        "## Long Short Term Memory (LSTM) Network for Sentiment Analysis\n",
        "\n",
        "LSTM model is a recurrent neural network (RNN) architecture that processes the entire sequence of data, not only a single point (as in the standard feed-forward neural networks).\n",
        "\n",
        "In this script, word embeddings are trained from text as a part of the model. It is a simple LSTM architecture that produces slightly better result than the baseline."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbYJdjVnizui"
      },
      "source": [
        "from keras.layers import Dense, Embedding, LSTM\n",
        "from keras.models import Sequential\n",
        "from keras.optimizers import RMSprop"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-kXcDtmbxg3"
      },
      "source": [
        "reviews_ind = load_pickle('data/reviews_ind_100k.pickle')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_GfVevNx-My",
        "outputId": "de667965-392f-4f60-9c8a-d1a15056d13d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "reviews_length = [len(r) for r in reviews_ind]\n",
        "print('Average review length: %0.2f, Standard deviation of reviews\\'s length: %0.2f' % (np.mean(reviews_length), np.std(reviews_length)))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average review length: 16.97, Standard deviation of reviews's length: 27.77\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpSzTVcYbxjB",
        "outputId": "a8c31bf9-1249-4508-bc4b-c56d627fe339",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from keras.preprocessing import sequence\n",
        "\n",
        "max_features = 6000\n",
        "max_len = 50\n",
        "\n",
        "reviews_ind = [[ind for ind in text_ind if ind < max_features] for text_ind in reviews_ind]\n",
        "reviews_pad = sequence.pad_sequences(reviews_ind, maxlen=max_len)\n",
        "\n",
        "X = np.stack(reviews_pad)\n",
        "\n",
        "print('reviews shape:', reviews_pad.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "reviews shape: (100000, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyJwW5POYyOw",
        "outputId": "2ef0625e-a4fa-4111-f1f4-0ae3fa9fab0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model_lstm = None\n",
        "\n",
        "model_lstm = Sequential()\n",
        "model_lstm.add(Embedding(max_features, 32))\n",
        "model_lstm.add(LSTM(32))\n",
        "model_lstm.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model_lstm.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=metrics_definition)\n",
        "\n",
        "history_lstm = model_lstm.fit(X, y,\n",
        "                        epochs=10,\n",
        "                        batch_size=128,\n",
        "                        validation_split=0.2)\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "625/625 [==============================] - 21s 33ms/step - loss: 0.1801 - accuracy: 0.9356 - precision: 0.9493 - recall: 0.9879 - val_loss: 0.1348 - val_accuracy: 0.9521 - val_precision: 0.9638 - val_recall: 0.9845\n",
            "Epoch 2/10\n",
            "625/625 [==============================] - 20s 32ms/step - loss: 0.1158 - accuracy: 0.9580 - precision: 0.9696 - recall: 0.9849 - val_loss: 0.1216 - val_accuracy: 0.9566 - val_precision: 0.9680 - val_recall: 0.9850\n",
            "Epoch 3/10\n",
            "625/625 [==============================] - 20s 32ms/step - loss: 0.1056 - accuracy: 0.9617 - precision: 0.9722 - recall: 0.9864 - val_loss: 0.1210 - val_accuracy: 0.9567 - val_precision: 0.9725 - val_recall: 0.9802\n",
            "Epoch 4/10\n",
            "625/625 [==============================] - 20s 32ms/step - loss: 0.0995 - accuracy: 0.9643 - precision: 0.9741 - recall: 0.9872 - val_loss: 0.1171 - val_accuracy: 0.9584 - val_precision: 0.9658 - val_recall: 0.9894\n",
            "Epoch 5/10\n",
            "625/625 [==============================] - 19s 31ms/step - loss: 0.0948 - accuracy: 0.9660 - precision: 0.9754 - recall: 0.9878 - val_loss: 0.1149 - val_accuracy: 0.9592 - val_precision: 0.9695 - val_recall: 0.9863\n",
            "Epoch 6/10\n",
            "625/625 [==============================] - 20s 31ms/step - loss: 0.0907 - accuracy: 0.9678 - precision: 0.9768 - recall: 0.9883 - val_loss: 0.1246 - val_accuracy: 0.9585 - val_precision: 0.9649 - val_recall: 0.9906\n",
            "Epoch 7/10\n",
            "625/625 [==============================] - 19s 31ms/step - loss: 0.0875 - accuracy: 0.9695 - precision: 0.9778 - recall: 0.9892 - val_loss: 0.1174 - val_accuracy: 0.9592 - val_precision: 0.9699 - val_recall: 0.9860\n",
            "Epoch 8/10\n",
            "625/625 [==============================] - 20s 31ms/step - loss: 0.0842 - accuracy: 0.9705 - precision: 0.9786 - recall: 0.9893 - val_loss: 0.1247 - val_accuracy: 0.9574 - val_precision: 0.9706 - val_recall: 0.9832\n",
            "Epoch 9/10\n",
            "625/625 [==============================] - 19s 31ms/step - loss: 0.0816 - accuracy: 0.9720 - precision: 0.9795 - recall: 0.9901 - val_loss: 0.1152 - val_accuracy: 0.9592 - val_precision: 0.9740 - val_recall: 0.9815\n",
            "Epoch 10/10\n",
            "625/625 [==============================] - 19s 31ms/step - loss: 0.0787 - accuracy: 0.9725 - precision: 0.9802 - recall: 0.9899 - val_loss: 0.1175 - val_accuracy: 0.9593 - val_precision: 0.9700 - val_recall: 0.9859\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrLRULaOadGH",
        "outputId": "913c2170-e92d-4b60-ff83-3953a9ea0d25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "model_report(history_lstm)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The highest accuracy score in 10 epoch: 0.959300\n",
            "The highest f1 score in 10 epoch: 0.977891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vHz87wHyHM5"
      },
      "source": [
        "## Convolutional Neural Network (CNN) for Sentiment Analysis\n",
        "\n",
        "CNNs are the core of most Computer Vision systems today but using them in NLP problems gives some interesting results as well.\n",
        "\n",
        "Although this model is more complex than the ones above, it doesn't outperform the previous ones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOG0YIELyIsm",
        "outputId": "09744564-45e0-439e-a2b1-666b19581168",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model_conv = None\n",
        "\n",
        "model_conv = Sequential()\n",
        "model_conv.add(layers.Embedding(max_features, 128, input_length=max_len))\n",
        "model_conv.add(layers.Conv1D(32, 7, activation='relu'))\n",
        "model_conv.add(layers.MaxPooling1D(5))\n",
        "model_conv.add(layers.Conv1D(32, 7, activation='relu'))\n",
        "model_conv.add(layers.GlobalMaxPooling1D())\n",
        "model_conv.add(layers.Dense(1, activation='sigmoid'))\n",
        "model_conv.summary()\n",
        "\n",
        "model_conv.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=metrics_definition)\n",
        "\n",
        "history_conv = model_conv.fit(X, y,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_split=0.33)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 50, 128)           768000    \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 44, 32)            28704     \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 8, 32)             0         \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 2, 32)             7200      \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d (Global (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 803,937\n",
            "Trainable params: 803,937\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "131/131 [==============================] - 25s 191ms/step - loss: 0.2681 - accuracy: 0.9137 - precision: 0.9300 - recall: 0.9919 - val_loss: 0.2222 - val_accuracy: 0.9280 - val_precision: 0.9346 - val_recall: 0.9904\n",
            "Epoch 2/20\n",
            "131/131 [==============================] - 26s 196ms/step - loss: 0.1860 - accuracy: 0.9384 - precision: 0.9443 - recall: 0.9912 - val_loss: 0.1834 - val_accuracy: 0.9382 - val_precision: 0.9455 - val_recall: 0.9894\n",
            "Epoch 3/20\n",
            "131/131 [==============================] - 26s 195ms/step - loss: 0.1608 - accuracy: 0.9476 - precision: 0.9545 - recall: 0.9899 - val_loss: 0.1793 - val_accuracy: 0.9409 - val_precision: 0.9507 - val_recall: 0.9864\n",
            "Epoch 4/20\n",
            "131/131 [==============================] - 25s 195ms/step - loss: 0.1487 - accuracy: 0.9524 - precision: 0.9589 - recall: 0.9904 - val_loss: 0.1805 - val_accuracy: 0.9424 - val_precision: 0.9502 - val_recall: 0.9886\n",
            "Epoch 5/20\n",
            "131/131 [==============================] - 26s 195ms/step - loss: 0.1380 - accuracy: 0.9567 - precision: 0.9626 - recall: 0.9913 - val_loss: 0.1832 - val_accuracy: 0.9417 - val_precision: 0.9542 - val_recall: 0.9833\n",
            "Epoch 6/20\n",
            "131/131 [==============================] - 26s 199ms/step - loss: 0.1263 - accuracy: 0.9621 - precision: 0.9669 - recall: 0.9927 - val_loss: 0.1913 - val_accuracy: 0.9390 - val_precision: 0.9562 - val_recall: 0.9780\n",
            "Epoch 7/20\n",
            "131/131 [==============================] - 26s 199ms/step - loss: 0.1161 - accuracy: 0.9661 - precision: 0.9699 - recall: 0.9938 - val_loss: 0.1997 - val_accuracy: 0.9412 - val_precision: 0.9505 - val_recall: 0.9870\n",
            "Epoch 8/20\n",
            "131/131 [==============================] - 26s 201ms/step - loss: 0.1070 - accuracy: 0.9691 - precision: 0.9724 - recall: 0.9945 - val_loss: 0.2074 - val_accuracy: 0.9385 - val_precision: 0.9517 - val_recall: 0.9826\n",
            "Epoch 9/20\n",
            "131/131 [==============================] - 26s 200ms/step - loss: 0.0999 - accuracy: 0.9719 - precision: 0.9746 - recall: 0.9952 - val_loss: 0.2188 - val_accuracy: 0.9349 - val_precision: 0.9563 - val_recall: 0.9732\n",
            "Epoch 10/20\n",
            "131/131 [==============================] - 26s 200ms/step - loss: 0.0934 - accuracy: 0.9741 - precision: 0.9763 - recall: 0.9959 - val_loss: 0.2369 - val_accuracy: 0.9323 - val_precision: 0.9567 - val_recall: 0.9697\n",
            "Epoch 11/20\n",
            "131/131 [==============================] - 26s 200ms/step - loss: 0.0880 - accuracy: 0.9764 - precision: 0.9780 - recall: 0.9966 - val_loss: 0.2526 - val_accuracy: 0.9362 - val_precision: 0.9511 - val_recall: 0.9806\n",
            "Epoch 12/20\n",
            "131/131 [==============================] - 26s 200ms/step - loss: 0.0838 - accuracy: 0.9779 - precision: 0.9790 - recall: 0.9972 - val_loss: 0.2618 - val_accuracy: 0.9324 - val_precision: 0.9552 - val_recall: 0.9715\n",
            "Epoch 13/20\n",
            "131/131 [==============================] - 26s 201ms/step - loss: 0.0806 - accuracy: 0.9785 - precision: 0.9796 - recall: 0.9974 - val_loss: 0.2783 - val_accuracy: 0.9322 - val_precision: 0.9535 - val_recall: 0.9733\n",
            "Epoch 14/20\n",
            "131/131 [==============================] - 26s 201ms/step - loss: 0.0780 - accuracy: 0.9792 - precision: 0.9801 - recall: 0.9975 - val_loss: 0.2953 - val_accuracy: 0.9291 - val_precision: 0.9555 - val_recall: 0.9674\n",
            "Epoch 15/20\n",
            "131/131 [==============================] - 27s 203ms/step - loss: 0.0751 - accuracy: 0.9804 - precision: 0.9809 - recall: 0.9980 - val_loss: 0.3093 - val_accuracy: 0.9290 - val_precision: 0.9547 - val_recall: 0.9682\n",
            "Epoch 16/20\n",
            "131/131 [==============================] - 26s 202ms/step - loss: 0.0733 - accuracy: 0.9812 - precision: 0.9815 - recall: 0.9982 - val_loss: 0.3291 - val_accuracy: 0.9288 - val_precision: 0.9541 - val_recall: 0.9686\n",
            "Epoch 17/20\n",
            "131/131 [==============================] - 27s 204ms/step - loss: 0.0720 - accuracy: 0.9818 - precision: 0.9819 - recall: 0.9985 - val_loss: 0.3567 - val_accuracy: 0.9224 - val_precision: 0.9565 - val_recall: 0.9585\n",
            "Epoch 18/20\n",
            "131/131 [==============================] - 27s 203ms/step - loss: 0.0723 - accuracy: 0.9819 - precision: 0.9822 - recall: 0.9984 - val_loss: 0.3565 - val_accuracy: 0.9255 - val_precision: 0.9549 - val_recall: 0.9640\n",
            "Epoch 19/20\n",
            "131/131 [==============================] - 26s 202ms/step - loss: 0.0705 - accuracy: 0.9821 - precision: 0.9824 - recall: 0.9984 - val_loss: 0.3876 - val_accuracy: 0.9326 - val_precision: 0.9473 - val_recall: 0.9807\n",
            "Epoch 20/20\n",
            "131/131 [==============================] - 27s 203ms/step - loss: 0.0690 - accuracy: 0.9827 - precision: 0.9827 - recall: 0.9987 - val_loss: 0.3919 - val_accuracy: 0.9237 - val_precision: 0.9536 - val_recall: 0.9634\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWlk3z7WyzUM",
        "outputId": "c3a37629-0d18-45e4-ea6c-e764a1c90a27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "model_report(history_conv)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The highest accuracy score in 4 epoch: 0.942364\n",
            "The highest f1 score in 4 epoch: 0.969061\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Gq1PT3gxyPQ"
      },
      "source": [
        "## Model verification\n",
        "\n",
        "LSTM model returned the best results: accuracy - 95,93%, f1-score - 0.9779. Let's take a look at the output given by this model.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crP6dVenzWJu"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cv37uQBQCEGm"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=9)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jmtKvWtymG5",
        "outputId": "e5c60587-7ff2-4736-c790-94e650b108db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(max_features, 32))\n",
        "model.add(LSTM(32))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=metrics_definition)\n",
        "\n",
        "model.fit(X_train, y_train,\n",
        "          epochs=8,\n",
        "          batch_size=128,\n",
        "          validation_split=0.2,\n",
        "          verbose=0)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbdfd70ee48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eear8jsl0DvU"
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred = np.where(y_pred>0.5, 1, 0)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEsne8yKZKS3",
        "outputId": "84efbecb-f195-4377-9c98-81c1fb68198d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
        "confusion_dict = {ax1: 'true', ax2: 'pred'}\n",
        "labels = ['negative', 'positive']\n",
        "\n",
        "for k, v in confusion_dict.items():\n",
        "    cm = confusion_matrix(y_test, y_pred, normalize=v)\n",
        "    sns.heatmap(cm,\n",
        "                cmap=plt.cm.Blues,\n",
        "                annot=True,\n",
        "                xticklabels=labels,\n",
        "                yticklabels=labels,\n",
        "                ax=k)\n",
        "    k.set_title('Confusion matrix with \\'%s\\' normalization' % v)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32-M9pwqbgEi"
      },
      "source": [
        "The model is very good at finding positive reviews - from all positive reviews, over 99% were assigned correct label. When it comes to negative reviews, the model assign correct label only in 61% of cases.\n",
        "\n",
        "When we look at the predicted labels, from all positive labels, 97% are true positives and 3,5% are false positives. Among labels predicted as negative, 85% are true negatives and 15% are false negatives.\n",
        "\n",
        "When comparing to Logistic Regression model we can see that the model has better results in assigning true negatives but it generates more false negatives at the same time. Despite this, model results are very similar to each other.\n",
        "\n",
        "There is a trade-off between precision and recall and finding a balance between those two should be based on a business decision. It is important to have in mind a cost of false negatives and false positives. The output of LSTM model are probabilities - for this analysis the 0.5 threshold was used but changing it will enable adjusting precision - recall ratio to the business needs."
      ]
    }
  ]
}