Sentiment_Analysis_02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_counts = Counter()\n",
    "negative_counts = Counter()\n",
    "total_counts = Counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 11,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 74074)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_0 = np.zeros((1, vocab_size))\n",
    "layer_0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74073"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_map_index[word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 90,
     "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": 95,
   "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": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_target_for_label(labels[0])\n",
    "#print(labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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