naive_bayes_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import movie_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# form a document with all reviews and their categories(class labels)\n",
    "documents = [(list(movie_reviews.words(file_id)), category)\n",
    "             for category in movie_reviews.categories()\n",
    "             for file_id in movie_reviews.fileids(category)]\n",
    "random.shuffle(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the frequency of all words in complete documents and take top 2000 words\n",
    "all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())\n",
    "word_features = list(all_words)[:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract feature vector for a document.(Feature for each word is indication whether the document contains that word)\n",
    "def document_features(document):\n",
    "    document_word = set(document)  # set of words in a document for faster computation\n",
    "    features = {}  # size = (2000,)\n",
    "    for word in word_features:\n",
    "        features['contains({})'.format(word)] = word in document_word  # check if each word in word_features is present in a document\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Naive Bayes classifier\n",
    "feature_sets = [(document_features(d), c) for (d, c) in documents]\n",
    "train_set, test_set = feature_sets[100:], feature_sets[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier accuracy: 0.79\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "print(\"Classifier accuracy: {}\\n\".format(nltk.classify.accuracy(classifier, test_set)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "   contains(outstanding) = True              pos : neg    =     10.6 : 1.0\n",
      "         contains(mulan) = True              pos : neg    =      9.1 : 1.0\n",
      "        contains(seagal) = True              neg : pos    =      8.1 : 1.0\n",
      "         contains(damon) = True              pos : neg    =      8.0 : 1.0\n",
      "   contains(wonderfully) = True              pos : neg    =      7.6 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier.show_most_informative_features(5)"
   ]
  }
 ],
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