Sentiment_Analysis_06.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = open('reviews.txt','r')\n",
    "reviews = list(map(lambda x:x[:-1], g.readlines()))\n",
    "g.close()\n",
    "\n",
    "g = open('labels.txt','r')\n",
    "labels = list(map(lambda x:x[:-1].lower(), g.readlines()))\n",
    "g.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_counts = Counter()\n",
    "negative_counts = Counter()\n",
    "total_counts = Counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(reviews)):\n",
    "    if(labels[i] == 'positive'):\n",
    "        for word in reviews[i].split(\" \"):\n",
    "            positive_counts[word] += 1\n",
    "            total_counts[word] += 1\n",
    "            \n",
    "    else:\n",
    "        for word in reviews[i].split(\" \"):\n",
    "            negative_counts[word] += 1\n",
    "            total_counts[word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_neg_ratios = Counter()\n",
    "\n",
    "for term,cnt in list(total_counts.most_common()):\n",
    "    if(cnt > 100):\n",
    "        pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n",
    "        pos_neg_ratios[term] = pos_neg_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab size : 74074\n"
     ]
    }
   ],
   "source": [
    "vocab = set(total_counts.keys())\n",
    "vocab_size = len(vocab)\n",
    "print(\"vocab size :\",len(vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 74074)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_0 = np.zeros((1, vocab_size))\n",
    "layer_0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_map_index = {}\n",
    "for i,word in enumerate(vocab):\n",
    "    word_map_index[word] = i\n",
    "    \n",
    "#word_map_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74073"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_map_index[word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_input_layer(review):\n",
    "    global layer_0\n",
    "    \n",
    "    layer_0 *= 0\n",
    "    #print(layer_0)\n",
    "    for word in review.split(\" \"):\n",
    "        layer_0[0][word_map_index[word]] += 1\n",
    "        #print(layer_0[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from the array you can find how many times each word has appeared in the given review, For example \" \" has appeared 18 times\n",
    "#in the second review\n",
    "update_input_layer(reviews[1])\n",
    "layer_0\n",
    "#reviews[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target_for_label(label):\n",
    "    if(label == 'positive'):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_target_for_label(labels[0])\n",
    "#print(labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import sys\n",
    "import numpy as np\n",
    "\n",
    "# Encapsulate our neural network in a class\n",
    "class SentimentNetwork:\n",
    "    ## New for Project 6: added min_count and polarity_cutoff parameters\n",
    "    def __init__(self, reviews,labels,min_count = 10,polarity_cutoff = 0.1,hidden_nodes = 10, learning_rate = 0.1):\n",
    "        \"\"\"Create a SentimenNetwork with the given settings\n",
    "        Args:\n",
    "            reviews(list) - List of reviews used for training\n",
    "            labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n",
    "            min_count(int) - Words should only be added to the vocabulary \n",
    "                             if they occur more than this many times\n",
    "            polarity_cutoff(float) - The absolute value of a word's positive-to-negative\n",
    "                                     ratio must be at least this big to be considered.\n",
    "            hidden_nodes(int) - Number of nodes to create in the hidden layer\n",
    "            learning_rate(float) - Learning rate to use while training\n",
    "        \n",
    "        \"\"\"\n",
    "        # Assign a seed to our random number generator to ensure we get\n",
    "        # reproducable results during development \n",
    "        np.random.seed(1)\n",
    "\n",
    "        # process the reviews and their associated labels so that everything\n",
    "        # is ready for training\n",
    "        ## New for Project 6: added min_count and polarity_cutoff arguments to pre_process_data call\n",
    "        self.pre_process_data(reviews, labels, polarity_cutoff, min_count)\n",
    "        \n",
    "        # Build the network to have the number of hidden nodes and the learning rate that\n",
    "        # were passed into this initializer. Make the same number of input nodes as\n",
    "        # there are vocabulary words and create a single output node.\n",
    "        self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n",
    "\n",
    "    ## New for Project 6: added min_count and polarity_cutoff parameters\n",
    "    def pre_process_data(self, reviews, labels, polarity_cutoff, min_count):\n",
    "        \n",
    "        ## ----------------------------------------\n",
    "        ## New for Project 6: Calculate positive-to-negative ratios for words before\n",
    "        #                     building vocabulary\n",
    "        #\n",
    "        positive_counts = Counter()\n",
    "        negative_counts = Counter()\n",
    "        total_counts = Counter()\n",
    "\n",
    "        for i in range(len(reviews)):\n",
    "            if(labels[i] == 'positive'):\n",
    "                for word in reviews[i].split(\" \"):\n",
    "                    positive_counts[word] += 1\n",
    "                    total_counts[word] += 1\n",
    "            else:\n",
    "                for word in reviews[i].split(\" \"):\n",
    "                    negative_counts[word] += 1\n",
    "                    total_counts[word] += 1\n",
    "\n",
    "        pos_neg_ratios = Counter()\n",
    "\n",
    "        for term,cnt in list(total_counts.most_common()):\n",
    "            if(cnt >= 50):\n",
    "                pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n",
    "                pos_neg_ratios[term] = pos_neg_ratio\n",
    "\n",
    "        for word,ratio in pos_neg_ratios.most_common():\n",
    "            if(ratio > 1):\n",
    "                pos_neg_ratios[word] = np.log(ratio)\n",
    "            else:\n",
    "                pos_neg_ratios[word] = -np.log((1 / (ratio + 0.01)))\n",
    "        #\n",
    "        ## end New for Project 6\n",
    "        ## ----------------------------------------\n",
    "\n",
    "        # populate review_vocab with all of the words in the given reviews\n",
    "        review_vocab = set()\n",
    "        for review in reviews:\n",
    "            for word in review.split(\" \"):\n",
    "                ## New for Project 6: only add words that occur at least min_count times\n",
    "                #                     and for words with pos/neg ratios, only add words\n",
    "                #                     that meet the polarity_cutoff\n",
    "                if(total_counts[word] > min_count):\n",
    "                    if(word in pos_neg_ratios.keys()):\n",
    "                        if((pos_neg_ratios[word] >= polarity_cutoff) or (pos_neg_ratios[word] <= -polarity_cutoff)):\n",
    "                            review_vocab.add(word)\n",
    "                    else:\n",
    "                        review_vocab.add(word)\n",
    "\n",
    "        # Convert the vocabulary set to a list so we can access words via indices\n",
    "        self.review_vocab = list(review_vocab)\n",
    "        \n",
    "        # populate label_vocab with all of the words in the given labels.\n",
    "        label_vocab = set()\n",
    "        for label in labels:\n",
    "            label_vocab.add(label)\n",
    "        \n",
    "        # Convert the label vocabulary set to a list so we can access labels via indices\n",
    "        self.label_vocab = list(label_vocab)\n",
    "        \n",
    "        # Store the sizes of the review and label vocabularies.\n",
    "        self.review_vocab_size = len(self.review_vocab)\n",
    "        self.label_vocab_size = len(self.label_vocab)\n",
    "        \n",
    "        # Create a dictionary of words in the vocabulary mapped to index positions\n",
    "        self.word2index = {}\n",
    "        for i, word in enumerate(self.review_vocab):\n",
    "            self.word2index[word] = i\n",
    "        \n",
    "        # Create a dictionary of labels mapped to index positions\n",
    "        self.label2index = {}\n",
    "        for i, label in enumerate(self.label_vocab):\n",
    "            self.label2index[label] = i\n",
    "\n",
    "    def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Store the learning rate\n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "        # Initialize weights\n",
    "\n",
    "        # These are the weights between the input layer and the hidden layer.\n",
    "        self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n",
    "\n",
    "        # These are the weights between the hidden layer and the output layer.\n",
    "        self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n",
    "                                                (self.hidden_nodes, self.output_nodes))\n",
    "        \n",
    "        ## New for Project 5: Removed self.layer_0; added self.layer_1\n",
    "        # The input layer, a two-dimensional matrix with shape 1 x hidden_nodes\n",
    "        self.layer_1 = np.zeros((1,hidden_nodes))\n",
    "    \n",
    "    ## New for Project 5: Removed update_input_layer function\n",
    "    \n",
    "    def get_target_for_label(self,label):\n",
    "        if(label == 'positive'):\n",
    "            return 1\n",
    "        else:\n",
    "            return 0\n",
    "        \n",
    "    def sigmoid(self,x):\n",
    "        return 1 / (1 + np.exp(-x))\n",
    "    \n",
    "    def sigmoid_output_2_derivative(self,output):\n",
    "        return output * (1 - output)\n",
    "    \n",
    "    ## New for Project 5: changed name of first parameter form 'training_reviews' \n",
    "    #                     to 'training_reviews_raw'\n",
    "    def train(self, training_reviews_raw, training_labels):\n",
    "\n",
    "        ## New for Project 5: pre-process training reviews so we can deal \n",
    "        #                     directly with the indices of non-zero inputs\n",
    "        training_reviews = list()\n",
    "        for review in training_reviews_raw:\n",
    "            indices = set()\n",
    "            for word in review.split(\" \"):\n",
    "                if(word in self.word2index.keys()):\n",
    "                    indices.add(self.word2index[word])\n",
    "            training_reviews.append(list(indices))\n",
    "\n",
    "        # make sure out we have a matching number of reviews and labels\n",
    "        assert(len(training_reviews) == len(training_labels))\n",
    "        \n",
    "        # Keep track of correct predictions to display accuracy during training \n",
    "        correct_so_far = 0\n",
    "\n",
    "        # Remember when we started for printing time statistics\n",
    "        start = time.time()\n",
    "        \n",
    "        # loop through all the given reviews and run a forward and backward pass,\n",
    "        # updating weights for every item\n",
    "        for i in range(len(training_reviews)):\n",
    "            \n",
    "            # Get the next review and its correct label\n",
    "            review = training_reviews[i]\n",
    "            label = training_labels[i]\n",
    "            \n",
    "            #### Implement the forward pass here ####\n",
    "            ### Forward pass ###\n",
    "\n",
    "            ## New for Project 5: Removed call to 'update_input_layer' function\n",
    "            #                     because 'layer_0' is no longer used\n",
    "\n",
    "            # Hidden layer\n",
    "            ## New for Project 5: Add in only the weights for non-zero items\n",
    "            self.layer_1 *= 0\n",
    "            for index in review:\n",
    "                self.layer_1 += self.weights_0_1[index]\n",
    "\n",
    "            # Output layer\n",
    "            ## New for Project 5: changed to use 'self.layer_1' instead of 'local layer_1'\n",
    "            layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))            \n",
    "            \n",
    "            #### Implement the backward pass here ####\n",
    "            ### Backward pass ###\n",
    "\n",
    "            # Output error\n",
    "            layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n",
    "            layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n",
    "\n",
    "            # Backpropagated error\n",
    "            layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n",
    "            layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n",
    "\n",
    "            # Update the weights\n",
    "            ## New for Project 5: changed to use 'self.layer_1' instead of local 'layer_1'\n",
    "            self.weights_1_2 -= self.layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n",
    "            \n",
    "            ## New for Project 5: Only update the weights that were used in the forward pass\n",
    "            for index in review:\n",
    "                self.weights_0_1[index] -= layer_1_delta[0] * self.learning_rate # update input-to-hidden weights with gradient descent step\n",
    "\n",
    "            # Keep track of correct predictions.\n",
    "            if(layer_2 >= 0.5 and label == 'positive'):\n",
    "                correct_so_far += 1\n",
    "            elif(layer_2 < 0.5 and label == 'negative'):\n",
    "                correct_so_far += 1\n",
    "            \n",
    "            # For debug purposes, print out our prediction accuracy and speed \n",
    "            # throughout the training process. \n",
    "            elapsed_time = float(time.time() - start)\n",
    "            reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n",
    "            \n",
    "            sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n",
    "                             + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n",
    "                             + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n",
    "                             + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n",
    "            if(i % 2500 == 0):\n",
    "                print(\"\")\n",
    "    \n",
    "    def test(self, testing_reviews, testing_labels):\n",
    "        \"\"\"\n",
    "        Attempts to predict the labels for the given testing_reviews,\n",
    "        and uses the test_labels to calculate the accuracy of those predictions.\n",
    "        \"\"\"\n",
    "        \n",
    "        # keep track of how many correct predictions we make\n",
    "        correct = 0\n",
    "\n",
    "        # we'll time how many predictions per second we make\n",
    "        start = time.time()\n",
    "\n",
    "        # Loop through each of the given reviews and call run to predict\n",
    "        # its label. \n",
    "        for i in range(len(testing_reviews)):\n",
    "            pred = self.run(testing_reviews[i])\n",
    "            if(pred == testing_labels[i]):\n",
    "                correct += 1\n",
    "            \n",
    "            # For debug purposes, print out our prediction accuracy and speed \n",
    "            # throughout the prediction process. \n",
    "\n",
    "            elapsed_time = float(time.time() - start)\n",
    "            reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n",
    "            \n",
    "            sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n",
    "                             + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n",
    "                             + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n",
    "                             + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n",
    "    \n",
    "    def run(self, review):\n",
    "        \"\"\"\n",
    "        Returns a POSITIVE or NEGATIVE prediction for the given review.\n",
    "        \"\"\"\n",
    "        # Run a forward pass through the network, like in the \"train\" function.\n",
    "        \n",
    "        ## New for Project 5: Removed call to update_input_layer function\n",
    "        #                     because layer_0 is no longer used\n",
    "\n",
    "        # Hidden layer\n",
    "        ## New for Project 5: Identify the indices used in the review and then add\n",
    "        #                     just those weights to layer_1 \n",
    "        self.layer_1 *= 0\n",
    "        unique_indices = set()\n",
    "        for word in review.lower().split(\" \"):\n",
    "            if word in self.word2index.keys():\n",
    "                unique_indices.add(self.word2index[word])\n",
    "        for index in unique_indices:\n",
    "            self.layer_1 += self.weights_0_1[index]\n",
    "        \n",
    "        # Output layer\n",
    "        ## New for Project 5: changed to use self.layer_1 instead of local layer_1\n",
    "        layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n",
    "         \n",
    "        # Return POSITIVE for values above greater-than-or-equal-to 0.5 in the output layer;\n",
    "        # return NEGATIVE for other values\n",
    "        if(layer_2[0] >= 0.5):\n",
    "            return \"positive\"\n",
    "        else:\n",
    "            return \"negative\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Progress:0.00% Speed(reviews/sec):14.71 #Correct:1 #Trained:2 Training Accuracy:50.0%\r",
      "Progress:0.00% Speed(reviews/sec):27.41 #Correct:1 #Trained:3 Training Accuracy:33.3%\r",
      "Progress:0.01% Speed(reviews/sec):35.31 #Correct:1 #Trained:4 Training Accuracy:25.0%\r",
      "Progress:0.01% Speed(reviews/sec):45.47 #Correct:1 #Trained:5 Training Accuracy:20.0%\r",
      "Progress:0.02% Speed(reviews/sec):54.97 #Correct:1 #Trained:6 Training Accuracy:16.6%\r",
      "Progress:0.02% Speed(reviews/sec):64.55 #Correct:1 #Trained:7 Training Accuracy:14.2%\r",
      "Progress:0.02% Speed(reviews/sec):74.51 #Correct:2 #Trained:8 Training Accuracy:25.0%\r",
      "Progress:0.03% Speed(reviews/sec):84.25 #Correct:2 #Trained:9 Training Accuracy:22.2%\r",
      "Progress:0.03% Speed(reviews/sec):91.88 #Correct:3 #Trained:10 Training Accuracy:30.0%\r",
      "Progress:0.04% Speed(reviews/sec):100.0 #Correct:3 #Trained:11 Training Accuracy:27.2%\r",
      "Progress:0.04% Speed(reviews/sec):107.9 #Correct:3 #Trained:12 Training Accuracy:25.0%\r",
      "Progress:0.05% Speed(reviews/sec):116.5 #Correct:3 #Trained:13 Training Accuracy:23.0%\r",
      "Progress:0.05% Speed(reviews/sec):121.5 #Correct:4 #Trained:14 Training Accuracy:28.5%\r",
      "Progress:0.05% Speed(reviews/sec):123.9 #Correct:4 #Trained:15 Training Accuracy:26.6%\r",
      "Progress:0.06% Speed(reviews/sec):128.2 #Correct:5 #Trained:16 Training Accuracy:31.2%\r",
      "Progress:0.06% Speed(reviews/sec):132.3 #Correct:6 #Trained:17 Training Accuracy:35.2%\r",
      "Progress:0.07% Speed(reviews/sec):139.4 #Correct:6 #Trained:18 Training Accuracy:33.3%\r",
      "Progress:0.07% Speed(reviews/sec):145.2 #Correct:6 #Trained:19 Training Accuracy:31.5%\r",
      "Progress:0.07% Speed(reviews/sec):152.0 #Correct:6 #Trained:20 Training Accuracy:30.0%\r",
      "Progress:0.08% Speed(reviews/sec):157.5 #Correct:6 #Trained:21 Training Accuracy:28.5%\r",
      "Progress:0.08% Speed(reviews/sec):162.8 #Correct:6 #Trained:22 Training Accuracy:27.2%\r",
      "Progress:0.09% Speed(reviews/sec):169.3 #Correct:7 #Trained:23 Training Accuracy:30.4%\r",
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      "Progress:1.14% Speed(reviews/sec):578.4 #Correct:203 #Trained:275 Training Accuracy:73.8%\r",
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      "Progress:1.15% Speed(reviews/sec):574.1 #Correct:204 #Trained:277 Training Accuracy:73.6%\r",
      "Progress:1.15% Speed(reviews/sec):575.0 #Correct:205 #Trained:278 Training Accuracy:73.7%\r",
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      "Progress:1.16% Speed(reviews/sec):572.0 #Correct:207 #Trained:280 Training Accuracy:73.9%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress:10.4% Speed(reviews/sec):543.8 #Correct:1994 #Trained:2501 Training Accuracy:79.7%\n",
      "Progress:20.8% Speed(reviews/sec):612.5 #Correct:4063 #Trained:5001 Training Accuracy:81.2%\n",
      "Progress:31.2% Speed(reviews/sec):617.7 #Correct:6176 #Trained:7501 Training Accuracy:82.3%\n",
      "Progress:41.6% Speed(reviews/sec):613.3 #Correct:8336 #Trained:10001 Training Accuracy:83.3%\n",
      "Progress:52.0% Speed(reviews/sec):621.9 #Correct:10501 #Trained:12501 Training Accuracy:84.0%\n",
      "Progress:62.5% Speed(reviews/sec):623.7 #Correct:12641 #Trained:15001 Training Accuracy:84.2%\n",
      "Progress:72.9% Speed(reviews/sec):618.1 #Correct:14782 #Trained:17501 Training Accuracy:84.4%\n",
      "Progress:83.3% Speed(reviews/sec):601.5 #Correct:16954 #Trained:20001 Training Accuracy:84.7%\n",
      "Progress:93.7% Speed(reviews/sec):607.8 #Correct:19143 #Trained:22501 Training Accuracy:85.0%\n",
      "Progress:99.9% Speed(reviews/sec):601.1 #Correct:20461 #Trained:24000 Training Accuracy:85.2%"
     ]
    }
   ],
   "source": [
    "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.05,learning_rate=0.01)\n",
    "mlp.train(reviews[:-1000],labels[:-1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Progress:0.0% Speed(reviews/sec):0 #Correct:1 #Tested:1 Testing Accuracy:100.%\r",
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      "Progress:0.6% Speed(reviews/sec):1200. #Correct:6 #Tested:7 Testing Accuracy:85.7%\r",
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      "Progress:99.9% Speed(reviews/sec):816.6 #Correct:859 #Tested:1000 Testing Accuracy:85.9%"
     ]
    }
   ],
   "source": [
    "mlp.test(reviews[-1000:],labels[-1000:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Progress:1.76% Speed(reviews/sec):487.0 #Correct:310 #Trained:425 Training Accuracy:72.9%\r",
      "Progress:1.77% Speed(reviews/sec):487.1 #Correct:311 #Trained:426 Training Accuracy:73.0%\r",
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      "Progress:1.77% Speed(reviews/sec):487.1 #Correct:311 #Trained:428 Training Accuracy:72.6%\r",
      "Progress:1.78% Speed(reviews/sec):486.1 #Correct:312 #Trained:429 Training Accuracy:72.7%\r",
      "Progress:1.78% Speed(reviews/sec):486.1 #Correct:313 #Trained:430 Training Accuracy:72.7%\r",
      "Progress:1.79% Speed(reviews/sec):486.7 #Correct:313 #Trained:431 Training Accuracy:72.6%\r",
      "Progress:1.79% Speed(reviews/sec):485.6 #Correct:313 #Trained:432 Training Accuracy:72.4%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress:10.4% Speed(reviews/sec):553.8 #Correct:1962 #Trained:2501 Training Accuracy:78.4%\n",
      "Progress:20.8% Speed(reviews/sec):531.6 #Correct:4002 #Trained:5001 Training Accuracy:80.0%\n",
      "Progress:31.2% Speed(reviews/sec):523.1 #Correct:6120 #Trained:7501 Training Accuracy:81.5%\n",
      "Progress:41.6% Speed(reviews/sec):524.0 #Correct:8271 #Trained:10001 Training Accuracy:82.7%\n",
      "Progress:52.0% Speed(reviews/sec):520.6 #Correct:10431 #Trained:12501 Training Accuracy:83.4%\n",
      "Progress:62.5% Speed(reviews/sec):520.1 #Correct:12565 #Trained:15001 Training Accuracy:83.7%\n",
      "Progress:72.9% Speed(reviews/sec):514.7 #Correct:14670 #Trained:17501 Training Accuracy:83.8%\n",
      "Progress:83.3% Speed(reviews/sec):516.7 #Correct:16833 #Trained:20001 Training Accuracy:84.1%\n",
      "Progress:93.7% Speed(reviews/sec):511.5 #Correct:19015 #Trained:22501 Training Accuracy:84.5%\n",
      "Progress:99.9% Speed(reviews/sec):509.8 #Correct:20335 #Trained:24000 Training Accuracy:84.7%"
     ]
    }
   ],
   "source": [
    "mlp_full = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=0,polarity_cutoff=0,learning_rate=0.01)\n",
    "mlp_full.train(reviews[:-1000],labels[:-1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_most_similar_words(focus = \"horrible\"):\n",
    "    most_similar = Counter()\n",
    "\n",
    "    for word in mlp_full.word2index.keys():\n",
    "        most_similar[word] = np.dot(mlp_full.weights_0_1[mlp_full.word2index[word]],mlp_full.weights_0_1[mlp_full.word2index[focus]])\n",
    "    \n",
    "    return most_similar.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " ('several', 0.013028244887060276),\n",
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       " ('graphics', 0.013020850288881853),\n",
       " ('reflection', 0.013019243823940102),\n",
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       " ('elvira', 0.01300981163895706),\n",
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       " ('greater', 0.012664580396020158),\n",
       " ('sailing', 0.01266242458128243),\n",
       " ('images', 0.012661803048859879),\n",
       " ('copy', 0.012624649645734145),\n",
       " ('seat', 0.012610464273152513),\n",
       " ('eleven', 0.012602533659978888),\n",
       " ('riveting', 0.012591829460094524),\n",
       " ('boiled', 0.012588863529638757),\n",
       " ('academy', 0.012581996178142988),\n",
       " ('whilst', 0.01256984165329564),\n",
       " ('heaven', 0.012547361621330928),\n",
       " ('fruit', 0.012543513029693252),\n",
       " ('reviewer', 0.012534273375083897),\n",
       " ('cost', 0.012529643005796613),\n",
       " ('week', 0.012522845015008277),\n",
       " ('intriguing', 0.012508687653306346),\n",
       " ('streak', 0.01250775238520856),\n",
       " ('san', 0.01250213005821792),\n",
       " ('awareness', 0.01247644644201245),\n",
       " ('catching', 0.01246710859545153),\n",
       " ('kicks', 0.01245771493057059),\n",
       " ('complexities', 0.012454362663082466),\n",
       " ('draws', 0.012447753285125911),\n",
       " ('easily', 0.012444885855614886),\n",
       " ('ealing', 0.01244433925570893),\n",
       " ('psychopath', 0.012431259926282277),\n",
       " ('skin', 0.01242424854097357),\n",
       " ('creative', 0.01238671345249153),\n",
       " ('recognition', 0.012354025801439425),\n",
       " ('downey', 0.012348698765161134),\n",
       " ('symbolism', 0.012329925038271329),\n",
       " ('touches', 0.012328013470751468),\n",
       " ('everyday', 0.012324934809895906),\n",
       " ('achieves', 0.012314898707483498),\n",
       " ('outcast', 0.012313662230219676),\n",
       " ('overwhelmed', 0.012306633138869472),\n",
       " ...]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_most_similar_words(\"excellent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('worst', 0.1696610725904984),\n",
       " ('awful', 0.12026847019691243),\n",
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       " ('ridiculous', 0.05690548106893144),\n",
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       " ('entitled', 0.010335246001593065),\n",
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       " ('bits', 0.010276065293631172),\n",
       " ('alone', 0.010268492053981962),\n",
       " ('accents', 0.010263852094534691),\n",
       " ('inhabited', 0.010244117693024819),\n",
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       " ('g', 0.010223458175403783),\n",
       " ('box', 0.01020330432926576),\n",
       " ('term', 0.010199983044386091),\n",
       " ('behavior', 0.010198776124373237),\n",
       " ('tedium', 0.010190092201507218),\n",
       " ('intent', 0.010190038120698568),\n",
       " ('husband', 0.010189502265957835),\n",
       " ('presence', 0.010187192336074171),\n",
       " ('z', 0.010184318583214762),\n",
       " ('unappealing', 0.010146391189444362),\n",
       " ('much', 0.010136790117697147),\n",
       " ('tree', 0.010113534581593909),\n",
       " ('doctors', 0.010099854380484186),\n",
       " ('pi', 0.010095099419111339),\n",
       " ('rodney', 0.010090819798082386),\n",
       " ('franchise', 0.010089650929674203),\n",
       " ('piece', 0.010086011549585334),\n",
       " ('company', 0.010083539582601041),\n",
       " ('choppy', 0.010079223420593739),\n",
       " ('turned', 0.010069855547990125),\n",
       " ('test', 0.0100415053556139),\n",
       " ('ball', 0.01004094432360953),\n",
       " ('hated', 0.010035509058945862),\n",
       " ('bear', 0.010034272465057467),\n",
       " ('serves', 0.01002749517216923),\n",
       " ('leonard', 0.010022751390164692),\n",
       " ('deserved', 0.010022334081283373),\n",
       " ('part', 0.010016360436147445),\n",
       " ('opportunity', 0.010013126012646697),\n",
       " ('turning', 0.010011850960865772),\n",
       " ('overacting', 0.01000899471498021),\n",
       " ('refer', 0.010006488920574081),\n",
       " ('flies', 0.010006418749637624),\n",
       " ('uninvolving', 0.009999133897620822),\n",
       " ('produce', 0.009996201403801374),\n",
       " ('jumpy', 0.009994785580841516),\n",
       " ('die', 0.009991412905867114),\n",
       " ('root', 0.009974713500112831),\n",
       " ('insomnia', 0.009974464255528509),\n",
       " ('blatant', 0.009959662000566385),\n",
       " ('larry', 0.009955690536790254),\n",
       " ('threw', 0.009947396538844962),\n",
       " ('billed', 0.00992858187536709),\n",
       " ('bullets', 0.009928175897100591),\n",
       " ('intellectually', 0.009908138827878617),\n",
       " ('rip', 0.009901323399604074),\n",
       " ('stretching', 0.009901296969917265),\n",
       " ('protest', 0.009898455267562362),\n",
       " ('soldiers', 0.00989369238224492),\n",
       " ('flick', 0.00988706336497766),\n",
       " ('justin', 0.009862246602717558),\n",
       " ('highlights', 0.009858908802058631),\n",
       " ('move', 0.009853989980954036),\n",
       " ('merit', 0.009843120594996679),\n",
       " ('russian', 0.0098411717219841),\n",
       " ('security', 0.009837345033883107),\n",
       " ('idiotic', 0.009834123428814465),\n",
       " ('produced', 0.009829430757425794),\n",
       " ('king', 0.00982668723431757),\n",
       " ('magically', 0.009822884247682564),\n",
       " ('united', 0.009807084789070775),\n",
       " ('missile', 0.00979905781933485),\n",
       " ('unlikable', 0.009786915898648076),\n",
       " ('ignorant', 0.009773274317346101),\n",
       " ('amateur', 0.009767405987056112),\n",
       " ('bachelor', 0.009767342945540575),\n",
       " ('asylum', 0.009762733851977994),\n",
       " ('screw', 0.00975680985739272),\n",
       " ('report', 0.009747923269917238),\n",
       " ('dracula', 0.009746732339320559),\n",
       " ('removed', 0.009741651949942207),\n",
       " ('confess', 0.009716292521157325),\n",
       " ('brand', 0.009715253466090755),\n",
       " ('conspiracy', 0.0097116972290397),\n",
       " ('horribly', 0.009708378556425245),\n",
       " ('switch', 0.009702684093379552),\n",
       " ('jaws', 0.00968774555137131),\n",
       " ('unsuspecting', 0.009685342503584642),\n",
       " ('betty', 0.009677035213332469),\n",
       " ('forwarding', 0.009671119689319278),\n",
       " ('university', 0.009663671587814962),\n",
       " ('star', 0.009662325493180045),\n",
       " ('crawl', 0.009646431896859054),\n",
       " ('dopey', 0.009646086331585858),\n",
       " ('ruin', 0.009623010638545728),\n",
       " ('lifeless', 0.009622880727487996),\n",
       " ('flash', 0.009619362535965),\n",
       " ('whoever', 0.00961741289158754),\n",
       " ('coincidence', 0.009602459974140215),\n",
       " ('choosing', 0.009595110005106926),\n",
       " ('avid', 0.009590091328422267),\n",
       " ('intended', 0.009584698704167624),\n",
       " ('remained', 0.009583962817858383),\n",
       " ('c', 0.009573267668176245),\n",
       " ('waiting', 0.009556225869434883),\n",
       " ('cassie', 0.009548135444223812),\n",
       " ('garage', 0.009534954458783024),\n",
       " ('clarke', 0.009534544585569864),\n",
       " ('fortune', 0.009533039664830207),\n",
       " ('interminable', 0.009532815956355264),\n",
       " ('incessant', 0.009523548502684637),\n",
       " ('plots', 0.009522580549062456),\n",
       " ('danger', 0.00951712056546929),\n",
       " ('costumes', 0.00949801446675244),\n",
       " ('evidently', 0.009495215846701219),\n",
       " ('minus', 0.009491149517466126),\n",
       " ('reporters', 0.009483681104099086),\n",
       " ('israeli', 0.009475007718336469),\n",
       " ('failing', 0.009471184131397685),\n",
       " ('paying', 0.00946923440668513),\n",
       " ('godzilla', 0.009458691554843782),\n",
       " ('dumber', 0.009458290309292487),\n",
       " ('earn', 0.009447622492842495),\n",
       " ('slows', 0.009446746387248761),\n",
       " ('held', 0.009445273681791481),\n",
       " ('chase', 0.009443836261194629),\n",
       " ('lies', 0.009438396984503345),\n",
       " ('hands', 0.009438178161458916),\n",
       " ('grief', 0.009423849453410287),\n",
       " ('brains', 0.009418215341663216),\n",
       " ('tom', 0.009413043338434715),\n",
       " ('resurrected', 0.009408342343729056),\n",
       " ('asking', 0.009402102940345337),\n",
       " ('sleeps', 0.009401795188265826),\n",
       " ('porno', 0.009390720141396513),\n",
       " ('somehow', 0.009388926127086056),\n",
       " ('sarcasm', 0.009388606439390414),\n",
       " ('tie', 0.009385600936631162),\n",
       " ('fall', 0.009380164000893119),\n",
       " ('bring', 0.009379127354576154),\n",
       " ('rape', 0.009376085123074631),\n",
       " ('village', 0.0093684513318614),\n",
       " ('kitchen', 0.009364907146010959),\n",
       " ('concerned', 0.009361135323881137),\n",
       " ('republic', 0.009349942694876425),\n",
       " ('hell', 0.009340036070531712),\n",
       " ('inducing', 0.009338212979255354),\n",
       " ('stomach', 0.009337828638515849),\n",
       " ('shambles', 0.009333545732982973),\n",
       " ('virgin', 0.009331200133905595),\n",
       " ('extraneous', 0.009325041380035126),\n",
       " ('cameras', 0.009322946026797717),\n",
       " ('suffers', 0.009320492992482998),\n",
       " ('justified', 0.009316321747936314),\n",
       " ('plummer', 0.009294827328510391),\n",
       " ('ponderous', 0.00928803442372233),\n",
       " ('player', 0.00928022963454437),\n",
       " ('survivor', 0.009276702647212571),\n",
       " ('rainy', 0.00926970342181375),\n",
       " ('graces', 0.009262094496329129),\n",
       " ...]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_most_similar_words(\"terrible\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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