Movie Rating.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREATING CLEAN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tokenization using regular expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.tokenize import RegexpTokenizer\n",
    "tokenizer = RegexpTokenizer(r\"\\w+\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stopword Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "st= set(stopwords.words('english'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.stem.snowball import SnowballStemmer\n",
    "ss = SnowballStemmer('english')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_stem_data(review):\n",
    "    review=review.lower()\n",
    "    review=review.replace(\"<br /><br />\",\" \")\n",
    "    \n",
    "    #Tokenize\n",
    "    tokens = tokenizer.tokenize(review)\n",
    "    n_token = [w for w in tokens if w not in st]\n",
    "    stemm_token= [ss.stem(token) for token in n_token]\n",
    "    \n",
    "    cleaned_review=' '.join(stemm_token)\n",
    "    return cleaned_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text=\"not really sure what to make of this movie. very weird, very artsy. not the kind of movie you watch because it has a compelling plot or characters. more like the kind of movie that you can't stop watching because of the horrifically fascinating things happening on screen. although, the first time my wife watched this she couldn't make it all the way through... too disturbing for her. runs a bit long, but nonetheless a worthwhile viewing for those interested in very dark movies.\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'realli sure make movi weird artsi kind movi watch compel plot charact like kind movi stop watch horrif fascin thing happen screen although first time wife watch make way disturb run bit long nonetheless worthwhil view interest dark movi'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_stem_data(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = open('Cleaned_file.text','w',encoding=\"utf8\")\n",
    "\n",
    "with open(\"imbd_textX.text\",encoding=\"ISO-8859-1\") as f:\n",
    "    reviews = f.readlines()\n",
    "\n",
    "for review in reviews:\n",
    "    cleaned_review = get_stem_data(review)\n",
    "    print((cleaned_review),file=out)\n",
    "\n",
    "out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "def dic_rating(x):\n",
    "    a={}\n",
    "    for i in range(len(x)):\n",
    "        for j in word_tokenize(x[i]):\n",
    "            try:\n",
    "                a[j]+=1\n",
    "            except:\n",
    "                a[j]=1\n",
    "    return a\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Buiding Multinomial Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prior_prob(y_train,rating):\n",
    "    total_example=len(y_train)\n",
    "    class_example = np.sum(y_train==rating)\n",
    "    \n",
    "    return float((class_example)/(total_example))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cond_prob(x_train,y_train,word,rating):\n",
    "    x_filter= x_train[y_train==rating]\n",
    "    \n",
    "    a=dic_rating(x_filter)\n",
    "    try:\n",
    "        numerator = a[word] + 1\n",
    "    except:\n",
    "        numerator = 1 \n",
    "    s=0\n",
    "    for i in a:\n",
    "        s+=a[i]\n",
    "    denominator = s+ len(a)\n",
    "    return  float(numerator/(denominator))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(x_train,y_train,x_test):\n",
    "    \n",
    "    post_prob=[]\n",
    "    x_test=get_stem_data(x_test)\n",
    "    \n",
    "    for rating in np.unique(y_train):\n",
    "        likehood=1\n",
    "        \n",
    "        for word in word_tokenize(x_text):\n",
    "            cond= cond_prob(x_train,y_train,word,rating)\n",
    "            likehood*=cond\n",
    "       \n",
    "        prior= prior_prob(y_train,rating)\n",
    "        prob= float(likehood*prior)\n",
    "        post_prob.append(prob)\n",
    "     \n",
    "    pred = np.argmax(post_prob)\n",
    "    return pred\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "x = [\"This was awesome an awesome movie\",\n",
    "     \"Great movie! I liked it a lot\",\n",
    "     \"Happy Ending! awesome acting by the hero\",\n",
    "     \"loved it! truly great\",\n",
    "     \"bad not upto the mark\",\n",
    "     \"could have been better\",\n",
    "     \"Surely a Disappointing movie\"]\n",
    "y_train = np.array([1,1,1,1,0,0,0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['awesom awesom movi', 'great movi like lot',\n",
       "       'happi end awesom act hero', 'love truli great', 'bad upto mark',\n",
       "       'could better', 'sure disappoint movi'],\n",
       "      dtype='<U25')"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train=[]\n",
    "for i in x:\n",
    "    s=get_stem_data(i)\n",
    "    x_train.append(s)\n",
    "x_train=np.array(x_train)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " x_text=\"I was happy & happy and I loved the acting in the movie\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=predict(x_train,y_train,x_test)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"Cleaned_file.text\",encoding=\"ISO-8859-1\") as f:\n",
    "    reviews = f.readlines()\n",
    "\n",
    "for review in reviews:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
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   "cell_type": "code",
   "execution_count": 301,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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