d.RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much work on sentiment analysis has been done on short-form text data such as those derived from micro-blogging social media sites. Because of the succinct nature of these social media posts, it is perhaps easier to glean points of view than from longer pieces of text.\n",
    "I would therefore like to perform sentiment parsing of long-form text in the form of book reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will use book reviews scraped from various websites including Goodreads stored on Kaggle as CSV files. The goal is to use different machine learning models to parse sentiment (either positive or negative) from each review. For this supervised classification problem, I will compare the performance of a Long Short Term Memory neural network (notebook c.) and Recurrent Neural Network (this notebook) to a Naive Bayes model (notebook b.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "%matplotlib inline\n",
    "\n",
    "import datetime\n",
    "\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "\n",
    "import itertools\n",
    "\n",
    "\n",
    "import spacy\n",
    "import en_core_web_sm\n",
    "\n",
    "import funcs\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import losses\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras import regularizers\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import re\n",
    "import string\n",
    "\n",
    "PROJ_ROOT = os.path.join(os.pardir, os.pardir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/Users/teresiahkahura/Desktop/ds/SB-Capstone2/data/processed/reviews5.pkl'\n",
    "df = pd.read_pickle(path)\n",
    "\n",
    "assert df.shape == (3268, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I followed the same steps outlined in notebook c preprocessing the data in preparation for the LSTM neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using spacy to tokenize and lemmatize not including pronouns\n",
    "\n",
    "df['text'] = [funcs.lemmatize_text(sent) for sent in df['text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting the label column to an array in preparation to feed into neural network\n",
    "x = df[\"text\"]\n",
    "y = df[\"label\"].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using Tensorflow's tokenizer to filter out special characters and digits\n",
    "tokenizer = Tokenizer(lower=True, filters='\\d*!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n', split = ' ')\n",
    "tokenizer.fit_on_texts(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sequence_of_tokens(corpus):\n",
    "    \"\"\"\n",
    "    Takes a corpus of text as argument and returns its input size which will be used in creating the \n",
    "    neural network first layer.\n",
    "    Also returns a vector representation of the text. \n",
    "    \"\"\"\n",
    "    #getting the input size to encode in the neural networks first layer\n",
    "    total_words = len(tokenizer.word_index) + 1\n",
    "    \n",
    "    #converts texts into vector space \n",
    "    input_sequences = []\n",
    "    for line in corpus:\n",
    "        token_list = tokenizer.texts_to_sequences([line])[0]\n",
    "        input_sequences.append(token_list)\n",
    "    return input_sequences, total_words\n",
    "\n",
    "inp_sequences, total_words = get_sequence_of_tokens(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_padded_sequences(input_sequences):\n",
    "    \"\"\"\n",
    "    Takes vectorized representation of text as argument and pads sequences that are smaller \n",
    "    to the size of the longest sequence length. \n",
    "    \"\"\"\n",
    "    max_sequence_len = max([len(x) for x in input_sequences])\n",
    "    input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len,))\n",
    "   \n",
    "    predictors = input_sequences[:,:-1]\n",
    "    return predictors, max_sequence_len\n",
    "\n",
    "predictors, max_sequence_len = generate_padded_sequences(inp_sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splits the vectorized representations of text and labels into test and training sizes\n",
    "x_train, x_test, y_train, y_test = train_test_split(predictors, y, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the LSTM neural network, the simple RNN performs best with an even split between the test and training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up RNN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    \"\"\"\n",
    "    Builds layers of neural network.\n",
    "    \"\"\"\n",
    "    opt = Adam(lr=1e-4)\n",
    "    model = tf.keras.Sequential()\n",
    "    #establishes first embedding layer with the shape of the total_words variable and a batch size of 128\n",
    "    model.add(tf.keras.layers.Embedding(total_words, 128))\n",
    "    #The next layer is bidirectional RNN\n",
    "    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.SimpleRNN(32)))\n",
    "    model.add(tf.keras.layers.Dropout(0.1))\n",
    "    #The last layer has sigmoid activation function to convert predictions to 0 - 1 format \n",
    "    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy', tf.keras.metrics.AUC()])\n",
    "    \n",
    "    return model\n",
    "\n",
    "rnn_model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removes previous logs \n",
    "!rm -r /tmp/logs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saves the best performing model onto disk\n",
    "logdir = os.path.join(\"/tmp/logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "tb_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath='/tmp/sentiment_analysis_rnn.hdf5', \n",
    "                                                  verbose = 1, \n",
    "                                                  save_best_only= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " 1/46 [..............................] - ETA: 0s - loss: 0.7083 - accuracy: 0.5625 - auc: 0.4107WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n",
      " 2/46 [>.............................] - ETA: 4s - loss: 0.6865 - accuracy: 0.5781 - auc: 0.5095WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0822s vs `on_train_batch_end` time: 0.1251s). Check your callbacks.\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.7024 - accuracy: 0.4986 - auc: 0.4936\n",
      "Epoch 00001: val_loss improved from inf to 0.69459, saving model to /tmp/sentiment_analysis_rnn.hdf5\n",
      "46/46 [==============================] - 4s 97ms/step - loss: 0.7024 - accuracy: 0.4986 - auc: 0.4936 - val_loss: 0.6946 - val_accuracy: 0.4817 - val_auc: 0.5173\n",
      "Epoch 2/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.6735 - accuracy: 0.5918 - auc: 0.6357\n",
      "Epoch 00002: val_loss did not improve from 0.69459\n",
      "46/46 [==============================] - 4s 82ms/step - loss: 0.6735 - accuracy: 0.5918 - auc: 0.6357 - val_loss: 0.6950 - val_accuracy: 0.5244 - val_auc: 0.5369\n",
      "Epoch 3/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.6367 - accuracy: 0.6673 - auc: 0.7367\n",
      "Epoch 00003: val_loss improved from 0.69459 to 0.67645, saving model to /tmp/sentiment_analysis_rnn.hdf5\n",
      "46/46 [==============================] - 4s 81ms/step - loss: 0.6367 - accuracy: 0.6673 - auc: 0.7367 - val_loss: 0.6764 - val_accuracy: 0.5549 - val_auc: 0.5967\n",
      "Epoch 4/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.5742 - accuracy: 0.7626 - auc: 0.8599\n",
      "Epoch 00004: val_loss improved from 0.67645 to 0.66656, saving model to /tmp/sentiment_analysis_rnn.hdf5\n",
      "46/46 [==============================] - 4s 95ms/step - loss: 0.5742 - accuracy: 0.7626 - auc: 0.8599 - val_loss: 0.6666 - val_accuracy: 0.6280 - val_auc: 0.6324\n",
      "Epoch 5/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.4884 - accuracy: 0.8585 - auc: 0.9337\n",
      "Epoch 00005: val_loss did not improve from 0.66656\n",
      "46/46 [==============================] - 4s 88ms/step - loss: 0.4884 - accuracy: 0.8585 - auc: 0.9337 - val_loss: 0.6729 - val_accuracy: 0.5610 - val_auc: 0.6118\n",
      "Epoch 6/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.4117 - accuracy: 0.9014 - auc: 0.9656\n",
      "Epoch 00006: val_loss improved from 0.66656 to 0.65981, saving model to /tmp/sentiment_analysis_rnn.hdf5\n",
      "46/46 [==============================] - 4s 94ms/step - loss: 0.4117 - accuracy: 0.9014 - auc: 0.9656 - val_loss: 0.6598 - val_accuracy: 0.6098 - val_auc: 0.6429\n",
      "Epoch 7/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.3281 - accuracy: 0.9442 - auc: 0.9884\n",
      "Epoch 00007: val_loss did not improve from 0.65981\n",
      "46/46 [==============================] - 4s 96ms/step - loss: 0.3281 - accuracy: 0.9442 - auc: 0.9884 - val_loss: 0.6621 - val_accuracy: 0.6280 - val_auc: 0.6540\n",
      "Epoch 8/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.2656 - accuracy: 0.9565 - auc: 0.9917\n",
      "Epoch 00008: val_loss did not improve from 0.65981\n",
      "46/46 [==============================] - 5s 107ms/step - loss: 0.2656 - accuracy: 0.9565 - auc: 0.9917 - val_loss: 0.6691 - val_accuracy: 0.6159 - val_auc: 0.6623\n",
      "Epoch 9/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.2115 - accuracy: 0.9694 - auc: 0.9968\n",
      "Epoch 00009: val_loss improved from 0.65981 to 0.65269, saving model to /tmp/sentiment_analysis_rnn.hdf5\n",
      "46/46 [==============================] - 4s 95ms/step - loss: 0.2115 - accuracy: 0.9694 - auc: 0.9968 - val_loss: 0.6527 - val_accuracy: 0.6220 - val_auc: 0.6820\n",
      "Epoch 10/10\n",
      "46/46 [==============================] - ETA: 0s - loss: 0.1707 - accuracy: 0.9830 - auc: 0.9987\n",
      "Epoch 00010: val_loss improved from 0.65269 to 0.64659, saving model to /tmp/sentiment_analysis_rnn.hdf5\n",
      "46/46 [==============================] - 5s 99ms/step - loss: 0.1707 - accuracy: 0.9830 - auc: 0.9987 - val_loss: 0.6466 - val_accuracy: 0.6585 - val_auc: 0.6994\n"
     ]
    }
   ],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', patience=5)\n",
    "history_rnn = rnn_model.fit(x_train, y_train, epochs = 10, validation_split=0.1, \n",
    "                           callbacks = [tb_callback, checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simple RNN does not perform very well on this data set with fluctuating validation accuracy scores around the 5th and 8th epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_graphs(history, metric):\n",
    "    \"\"\"\n",
    "    Takes a model and associated metric and plots performance over the specified epochs\n",
    "    \"\"\"\n",
    "    plt.plot(history.history[metric])\n",
    "    plt.plot(history.history['val_'+ metric], '')\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([metric, 'val_'+ metric])\n",
    "    plt.show()\n",
    "\n",
    "plot_graphs(history_rnn, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(history_rnn, 'loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52/52 [==============================] - 1s 18ms/step - loss: 0.7308 - accuracy: 0.5741 - auc: 0.6186\n",
      "[0.7307701706886292, 0.5740513801574707, 0.6185731887817383]\n"
     ]
    }
   ],
   "source": [
    "results = rnn_model.evaluate(x_test, y_test)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is the poorest performing out of the three tried out so far on both accuracy and auc. The layers of this model differ from the LSTM model in the absence of regularization layer which might diminish the model's ability to learn on sparse data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(history_rnn, 'auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LSTM neural network outperformed all three models (accuracy 79%, AUC 86%) perhaps due to it’s more complex layers designed to optimize learning on sparse matrices. This was followed closely by the Bernoulli Naive Bayes classifier coming in at 78% accuracy and an AUC of 78% and the simple RNN trained in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During preprocessing, I discovered that my dataset had reviews in different languages including Spanish and Arabic and removing these resulted in a smaller corpus to train on. While the performance of our best model is decent, increased accuracy to distinguish positive sentiment can be achieved by training the model on more text. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['Naive Bayes','LSTM','Simple RNN']\n",
    "y = [78, 79, 57]\n",
    "colors = itertools.cycle(['g', 'c', 'y'])\n",
    "plt.ylabel('Accuracy score %')\n",
    "plt.xlabel('Model Details')\n",
    "plt.xticks(rotation=75)\n",
    "for i in range(len(y)):\n",
    "    plt.bar(x[i], y[i], color=next(colors), width = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['Naive Bayes','LSTM','Simple RNN']\n",
    "y = [78, 86, 62]\n",
    "colors = itertools.cycle(['g', 'c', 'y'])\n",
    "plt.ylabel('AUC score %')\n",
    "plt.xlabel('Model Details')\n",
    "plt.xticks(rotation=75)\n",
    "for i in range(len(y)):\n",
    "    plt.bar(x[i], y[i], color=next(colors), width = 0.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}