nltk_ch_06.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gender_features(word):\n",
    "     return {'last_letter': word[-1]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Classification\n",
    "Features are extracted from input training data set, then feature with correspondong label are fed to ML algo to generated classifier model. From testing data, again extracts feature but this time model will predict label.\n",
    "\n",
    "http://www.nltk.org/book/ch06.html#fig-supervised-classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labeled_names = ([(name, 'male') for name in names.words('male.txt')] + \n",
    "                 [(name, 'female') for name in names.words('female.txt')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "random.shuffle(labeled_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "featuresets = [(gender_features(n), gender) for (n, gender) in labeled_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n[({'last_letter': 'n'}, 'male'),\\n ({'last_letter': 't'}, 'female'),\\n ({'last_letter': 'a'}, 'female'),\\n\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# featuresets\n",
    "'''\n",
    "[({'last_letter': 'n'}, 'male'),\n",
    " ({'last_letter': 't'}, 'female'),\n",
    " ({'last_letter': 'a'}, 'female'),\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set, test_set = featuresets[500:], featuresets[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'male'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(gender_features('Neo'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'female'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(gender_features('Trinity'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.78\n"
     ]
    }
   ],
   "source": [
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "             last_letter = 'a'            female : male   =     35.7 : 1.0\n",
      "             last_letter = 'k'              male : female =     31.2 : 1.0\n",
      "             last_letter = 'f'              male : female =     15.9 : 1.0\n",
      "             last_letter = 'v'              male : female =     11.2 : 1.0\n",
      "             last_letter = 'p'              male : female =     10.5 : 1.0\n"
     ]
    }
   ],
   "source": [
    " classifier.show_most_informative_features(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This listing shows that the names in the training set that end in \"a\" are female 35 times more often than they are male, but names that end in \"k\" are male 30 times more often than they are female. These ratios are known as likelihood ratios, and can be useful for comparing different feature-outcome relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.classify import apply_features\n",
    "train_set = apply_features(gender_features, labeled_names[500:])\n",
    "test_set = apply_features(gender_features, labeled_names[:500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Right Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count(a)': 0,\n",
       " 'count(b)': 0,\n",
       " 'count(c)': 0,\n",
       " 'count(d)': 0,\n",
       " 'count(e)': 0,\n",
       " 'count(f)': 0,\n",
       " 'count(g)': 0,\n",
       " 'count(h)': 1,\n",
       " 'count(i)': 0,\n",
       " 'count(j)': 1,\n",
       " 'count(k)': 0,\n",
       " 'count(l)': 0,\n",
       " 'count(m)': 0,\n",
       " 'count(n)': 1,\n",
       " 'count(o)': 1,\n",
       " 'count(p)': 0,\n",
       " 'count(q)': 0,\n",
       " 'count(r)': 0,\n",
       " 'count(s)': 0,\n",
       " 'count(t)': 0,\n",
       " 'count(u)': 0,\n",
       " 'count(v)': 0,\n",
       " 'count(w)': 0,\n",
       " 'count(x)': 0,\n",
       " 'count(y)': 0,\n",
       " 'count(z)': 0,\n",
       " 'first_letter': 'j',\n",
       " 'has(a)': False,\n",
       " 'has(b)': False,\n",
       " 'has(c)': False,\n",
       " 'has(d)': False,\n",
       " 'has(e)': False,\n",
       " 'has(f)': False,\n",
       " 'has(g)': False,\n",
       " 'has(h)': True,\n",
       " 'has(i)': False,\n",
       " 'has(j)': True,\n",
       " 'has(k)': False,\n",
       " 'has(l)': False,\n",
       " 'has(m)': False,\n",
       " 'has(n)': True,\n",
       " 'has(o)': True,\n",
       " 'has(p)': False,\n",
       " 'has(q)': False,\n",
       " 'has(r)': False,\n",
       " 'has(s)': False,\n",
       " 'has(t)': False,\n",
       " 'has(u)': False,\n",
       " 'has(v)': False,\n",
       " 'has(w)': False,\n",
       " 'has(x)': False,\n",
       " 'has(y)': False,\n",
       " 'has(z)': False,\n",
       " 'last_letter': 'n'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gender_features2(name):\n",
    "    features = {}\n",
    "    features[\"first_letter\"] = name[0].lower()\n",
    "    features[\"last_letter\"] = name[-1].lower()\n",
    "    for letter in 'abcdefghijklmnopqrstuvwxyz':\n",
    "        features[\"count({})\".format(letter)] = name.lower().count(letter)\n",
    "        features[\"has({})\".format(letter)] = (letter in name.lower())\n",
    "    return features\n",
    "\n",
    "gender_features2('John') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "featuresets = [(gender_features2(n), gender) for (n, gender) in labeled_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set, test_set = featuresets[500:], featuresets[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.776\n"
     ]
    }
   ],
   "source": [
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_names = labeled_names[1500:]\n",
    "devtest_names = labeled_names[500:1500]\n",
    "test_names = labeled_names[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set = [(gender_features(n), gender) for (n, gender) in train_names]\n",
    "devtest_set = [(gender_features(n), gender) for (n, gender) in devtest_names]\n",
    "test_set = [(gender_features(n), gender) for (n, gender) in test_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.766\n"
     ]
    }
   ],
   "source": [
    "print(nltk.classify.accuracy(classifier, devtest_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "errors = []\n",
    "for (name, tag) in devtest_names:\n",
    "    guess = classifier.classify(gender_features(name))\n",
    "    if(guess != tag):\n",
    "        errors.append((name, tag, guess))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name=Adel, correct=female, guess=male\n",
      "name=Ag, correct=female, guess=male\n",
      "name=Aimil, correct=female, guess=male\n",
      "name=Alex, correct=female, guess=male\n",
      "name=Allison, correct=female, guess=male\n",
      "name=Alyss, correct=female, guess=male\n",
      "name=Ambrosi, correct=male, guess=female\n",
      "name=Anabel, correct=female, guess=male\n",
      "name=Andri, correct=male, guess=female\n",
      "name=Ann, correct=female, guess=male\n",
      "name=Arlyn, correct=female, guess=male\n",
      "name=Aubrey, correct=male, guess=female\n",
      "name=Austin, correct=female, guess=male\n",
      "name=Bailie, correct=male, guess=female\n",
      "name=Barnaby, correct=male, guess=female\n",
      "name=Bartholemy, correct=male, guess=female\n",
      "name=Beatrix, correct=female, guess=male\n",
      "name=Bell, correct=female, guess=male\n",
      "name=Berget, correct=female, guess=male\n",
      "name=Blair, correct=female, guess=male\n",
      "name=Blayne, correct=male, guess=female\n",
      "name=Brenn, correct=female, guess=male\n",
      "name=Brett, correct=female, guess=male\n",
      "name=Burke, correct=male, guess=female\n",
      "name=Butch, correct=male, guess=female\n",
      "name=Calypso, correct=female, guess=male\n",
      "name=Cameo, correct=female, guess=male\n",
      "name=Caril, correct=female, guess=male\n",
      "name=Carlin, correct=female, guess=male\n",
      "name=Chaddy, correct=male, guess=female\n",
      "name=Chancey, correct=male, guess=female\n",
      "name=Chandal, correct=female, guess=male\n",
      "name=Chauncey, correct=male, guess=female\n",
      "name=Che, correct=male, guess=female\n",
      "name=Cher, correct=female, guess=male\n",
      "name=Chriss, correct=female, guess=male\n",
      "name=Christal, correct=female, guess=male\n",
      "name=Christan, correct=female, guess=male\n",
      "name=Christen, correct=female, guess=male\n",
      "name=Christin, correct=female, guess=male\n",
      "name=Clyde, correct=male, guess=female\n",
      "name=Coleen, correct=female, guess=male\n",
      "name=Conway, correct=male, guess=female\n",
      "name=Cortese, correct=male, guess=female\n",
      "name=Cristal, correct=female, guess=male\n",
      "name=Cristin, correct=female, guess=male\n",
      "name=Cyrille, correct=male, guess=female\n",
      "name=Dael, correct=female, guess=male\n",
      "name=Danell, correct=female, guess=male\n",
      "name=Danny, correct=male, guess=female\n",
      "name=Daveen, correct=female, guess=male\n",
      "name=Davie, correct=male, guess=female\n",
      "name=Davy, correct=male, guess=female\n",
      "name=Demetri, correct=male, guess=female\n",
      "name=Dennie, correct=male, guess=female\n",
      "name=Derby, correct=male, guess=female\n",
      "name=Diamond, correct=female, guess=male\n",
      "name=Dion, correct=female, guess=male\n",
      "name=Donny, correct=male, guess=female\n",
      "name=Doralyn, correct=female, guess=male\n",
      "name=Dorit, correct=female, guess=male\n",
      "name=Dudley, correct=male, guess=female\n",
      "name=Durante, correct=male, guess=female\n",
      "name=Dwane, correct=male, guess=female\n",
      "name=Dyann, correct=female, guess=male\n",
      "name=Elisabet, correct=female, guess=male\n",
      "name=Ellyn, correct=female, guess=male\n",
      "name=Elmore, correct=male, guess=female\n",
      "name=Elroy, correct=male, guess=female\n",
      "name=Em, correct=female, guess=male\n",
      "name=Esme, correct=male, guess=female\n",
      "name=Ethelin, correct=female, guess=male\n",
      "name=Felipe, correct=male, guess=female\n",
      "name=Finley, correct=male, guess=female\n",
      "name=Fletch, correct=male, guess=female\n",
      "name=Fleur, correct=female, guess=male\n",
      "name=Flower, correct=female, guess=male\n",
      "name=Frances, correct=female, guess=male\n",
      "name=Friedrich, correct=male, guess=female\n",
      "name=Gen, correct=female, guess=male\n",
      "name=Gerri, correct=male, guess=female\n",
      "name=Ginnifer, correct=female, guess=male\n",
      "name=Giuseppe, correct=male, guess=female\n",
      "name=Granville, correct=male, guess=female\n",
      "name=Grethel, correct=female, guess=male\n",
      "name=Gunvor, correct=female, guess=male\n",
      "name=Guthrie, correct=male, guess=female\n",
      "name=Guthry, correct=male, guess=female\n",
      "name=Hadleigh, correct=male, guess=female\n",
      "name=Haleigh, correct=male, guess=female\n",
      "name=Harry, correct=male, guess=female\n",
      "name=Henrie, correct=male, guess=female\n",
      "name=Henrique, correct=male, guess=female\n",
      "name=Hildagard, correct=female, guess=male\n",
      "name=Hirsch, correct=male, guess=female\n",
      "name=Holly, correct=male, guess=female\n",
      "name=Horace, correct=male, guess=female\n",
      "name=Huntley, correct=male, guess=female\n",
      "name=Iggy, correct=male, guess=female\n",
      "name=Imogen, correct=female, guess=male\n",
      "name=Isaiah, correct=male, guess=female\n",
      "name=Isidore, correct=male, guess=female\n",
      "name=Jannel, correct=female, guess=male\n",
      "name=Jasmin, correct=female, guess=male\n",
      "name=Jeffery, correct=male, guess=female\n",
      "name=Jeffrey, correct=male, guess=female\n",
      "name=Jen, correct=female, guess=male\n",
      "name=Jessalin, correct=female, guess=male\n",
      "name=Jessalyn, correct=female, guess=male\n",
      "name=Joellen, correct=female, guess=male\n",
      "name=Jonny, correct=male, guess=female\n",
      "name=Jordain, correct=female, guess=male\n",
      "name=Jordy, correct=male, guess=female\n",
      "name=Jourdan, correct=female, guess=male\n",
      "name=Judah, correct=male, guess=female\n",
      "name=Kaitlyn, correct=female, guess=male\n",
      "name=Karlyn, correct=female, guess=male\n",
      "name=Kaster, correct=female, guess=male\n",
      "name=Katheryn, correct=female, guess=male\n",
      "name=Kathryn, correct=female, guess=male\n",
      "name=Kaylil, correct=female, guess=male\n",
      "name=Kelly, correct=male, guess=female\n",
      "name=Kenneth, correct=male, guess=female\n",
      "name=Keren, correct=female, guess=male\n",
      "name=Kingsley, correct=male, guess=female\n",
      "name=Lawrence, correct=male, guess=female\n",
      "name=Lay, correct=male, guess=female\n",
      "name=Lefty, correct=male, guess=female\n",
      "name=Leigh, correct=male, guess=female\n",
      "name=Lind, correct=female, guess=male\n",
      "name=Lindy, correct=male, guess=female\n",
      "name=Lonny, correct=male, guess=female\n",
      "name=Loreen, correct=female, guess=male\n",
      "name=Loren, correct=female, guess=male\n",
      "name=Luce, correct=male, guess=female\n",
      "name=Lurleen, correct=female, guess=male\n",
      "name=Lyn, correct=female, guess=male\n",
      "name=Lynn, correct=female, guess=male\n",
      "name=Madalyn, correct=female, guess=male\n",
      "name=Madelin, correct=female, guess=male\n",
      "name=Mag, correct=female, guess=male\n",
      "name=Magdalen, correct=female, guess=male\n",
      "name=Mairead, correct=female, guess=male\n",
      "name=Marabel, correct=female, guess=male\n",
      "name=Marie-Ann, correct=female, guess=male\n",
      "name=Marijo, correct=female, guess=male\n",
      "name=Marillin, correct=female, guess=male\n",
      "name=Marin, correct=female, guess=male\n",
      "name=Marsh, correct=male, guess=female\n",
      "name=Maryann, correct=female, guess=male\n",
      "name=Marys, correct=female, guess=male\n",
      "name=Mattie, correct=male, guess=female\n",
      "name=Maxie, correct=male, guess=female\n",
      "name=Meg, correct=female, guess=male\n",
      "name=Meghann, correct=female, guess=male\n",
      "name=Micah, correct=male, guess=female\n",
      "name=Mildrid, correct=female, guess=male\n",
      "name=Milicent, correct=female, guess=male\n",
      "name=Milissent, correct=female, guess=male\n",
      "name=Moishe, correct=male, guess=female\n",
      "name=Mordecai, correct=male, guess=female\n",
      "name=Morly, correct=male, guess=female\n",
      "name=Nadean, correct=female, guess=male\n",
      "name=Nichole, correct=male, guess=female\n",
      "name=Noe, correct=male, guess=female\n",
      "name=Orel, correct=female, guess=male\n",
      "name=Orville, correct=male, guess=female\n",
      "name=Pattie, correct=male, guess=female\n",
      "name=Peg, correct=female, guess=male\n",
      "name=Perry, correct=male, guess=female\n",
      "name=Petey, correct=male, guess=female\n",
      "name=Phylis, correct=female, guess=male\n",
      "name=Prentice, correct=male, guess=female\n",
      "name=Prince, correct=male, guess=female\n",
      "name=Raf, correct=female, guess=male\n",
      "name=Raychel, correct=female, guess=male\n",
      "name=Reece, correct=male, guess=female\n",
      "name=Renel, correct=female, guess=male\n",
      "name=Rich, correct=male, guess=female\n",
      "name=Robin, correct=female, guess=male\n",
      "name=Rodney, correct=male, guess=female\n",
      "name=Ronny, correct=male, guess=female\n",
      "name=Rosalynd, correct=female, guess=male\n",
      "name=Rosario, correct=female, guess=male\n",
      "name=Roselin, correct=female, guess=male\n",
      "name=Ruddie, correct=male, guess=female\n",
      "name=Rustie, correct=male, guess=female\n",
      "name=Sallyann, correct=female, guess=male\n",
      "name=Salomone, correct=male, guess=female\n",
      "name=Scarface, correct=male, guess=female\n",
      "name=Scotty, correct=male, guess=female\n",
      "name=Selby, correct=male, guess=female\n",
      "name=Sharron, correct=female, guess=male\n",
      "name=Shay, correct=male, guess=female\n",
      "name=Shea, correct=male, guess=female\n",
      "name=Shelley, correct=male, guess=female\n",
      "name=Shorty, correct=male, guess=female\n",
      "name=Sidnee, correct=male, guess=female\n",
      "name=Sileas, correct=female, guess=male\n",
      "name=Sinead, correct=female, guess=male\n",
      "name=Skelly, correct=male, guess=female\n",
      "name=Skye, correct=male, guess=female\n",
      "name=Smitty, correct=male, guess=female\n",
      "name=Solly, correct=male, guess=female\n",
      "name=Stanley, correct=male, guess=female\n",
      "name=Suellen, correct=female, guess=male\n",
      "name=Susan, correct=female, guess=male\n",
      "name=Sydney, correct=male, guess=female\n",
      "name=Tallie, correct=male, guess=female\n",
      "name=Teryl, correct=female, guess=male\n",
      "name=Tess, correct=female, guess=male\n",
      "name=Thayne, correct=male, guess=female\n",
      "name=Thorndike, correct=male, guess=female\n",
      "name=Thorpe, correct=male, guess=female\n",
      "name=Tomiko, correct=female, guess=male\n",
      "name=Tommie, correct=male, guess=female\n",
      "name=Tonnie, correct=male, guess=female\n",
      "name=Torey, correct=male, guess=female\n",
      "name=Torre, correct=male, guess=female\n",
      "name=Tuckie, correct=male, guess=female\n",
      "name=Vasily, correct=male, guess=female\n",
      "name=Verney, correct=male, guess=female\n",
      "name=Vin, correct=female, guess=male\n",
      "name=Waine, correct=male, guess=female\n",
      "name=Waverley, correct=male, guess=female\n",
      "name=Way, correct=male, guess=female\n",
      "name=Westbrooke, correct=male, guess=female\n",
      "name=Westleigh, correct=male, guess=female\n",
      "name=Winnie, correct=male, guess=female\n",
      "name=Wojciech, correct=male, guess=female\n",
      "name=Yehudi, correct=male, guess=female\n",
      "name=Yule, correct=male, guess=female\n",
      "name=Zacherie, correct=male, guess=female\n",
      "name=Zedekiah, correct=male, guess=female\n"
     ]
    }
   ],
   "source": [
    "for(name, tag, guess) in sorted(errors):\n",
    "    print(\"name={}, correct={}, guess={}\".format(name, tag, guess))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Document Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import movie_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "documents = [(list(movie_reviews.words(fileid)), category) \n",
    "             for category in movie_reviews.categories()\n",
    "             for fileid in movie_reviews.fileids(category)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "random.shuffle(documents)\n",
    "#documents[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neg', 'pos']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_reviews.categories()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#movie_reviews.fileids('neg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get top 2000 frequent words used in corpus to restrict the dictionary\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": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def document_features(document):\n",
    "    document_words = set(document)\n",
    "    features = {}\n",
    "    for word in word_features:\n",
    "        features['contains({})'.format(word)] = (word in document_words)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'contains(plot)': True, 'contains(:)': True, 'contains(two)': True, 'contains(teen)': False, 'contains(couples)': False, 'contains(go)': False, 'contains(to)': True, 'contains(a)': True, 'contains(church)': False, 'contains(party)': False, 'contains(,)': True, 'contains(drink)': False, 'contains(and)': True, 'contains(then)': True, 'contains(drive)': False, 'contains(.)': True, 'contains(they)': True, 'contains(get)': True, 'contains(into)': True, 'contains(an)': True, 'contains(accident)': False, 'contains(one)': True, 'contains(of)': True, 'contains(the)': True, 'contains(guys)': False, 'contains(dies)': False, 'contains(but)': True, 'contains(his)': True, 'contains(girlfriend)': True, 'contains(continues)': False, 'contains(see)': False, 'contains(him)': True, 'contains(in)': True, 'contains(her)': False, 'contains(life)': False, 'contains(has)': True, 'contains(nightmares)': False, 'contains(what)': True, \"contains(')\": True, 'contains(s)': True, 'contains(deal)': False, 'contains(?)': False, 'contains(watch)': True, 'contains(movie)': True, 'contains(\")': True, 'contains(sorta)': False, 'contains(find)': False, 'contains(out)': True, 'contains(critique)': False, 'contains(mind)': False, 'contains(-)': True, 'contains(fuck)': False, 'contains(for)': True, 'contains(generation)': False, 'contains(that)': True, 'contains(touches)': False, 'contains(on)': True, 'contains(very)': True, 'contains(cool)': False, 'contains(idea)': True, 'contains(presents)': False, 'contains(it)': True, 'contains(bad)': False, 'contains(package)': False, 'contains(which)': True, 'contains(is)': True, 'contains(makes)': False, 'contains(this)': True, 'contains(review)': False, 'contains(even)': False, 'contains(harder)': False, 'contains(write)': False, 'contains(since)': False, 'contains(i)': False, 'contains(generally)': False, 'contains(applaud)': False, 'contains(films)': False, 'contains(attempt)': False, 'contains(break)': False, 'contains(mold)': False, 'contains(mess)': False, 'contains(with)': True, 'contains(your)': False, 'contains(head)': False, 'contains(such)': False, 'contains(()': True, 'contains(lost)': False, 'contains(highway)': False, 'contains(&)': False, 'contains(memento)': False, 'contains())': True, 'contains(there)': True, 'contains(are)': True, 'contains(good)': False, 'contains(ways)': False, 'contains(making)': True, 'contains(all)': True, 'contains(types)': False, 'contains(these)': False, 'contains(folks)': False, 'contains(just)': True, 'contains(didn)': False, 'contains(t)': False, 'contains(snag)': False, 'contains(correctly)': False, 'contains(seem)': False, 'contains(have)': True, 'contains(taken)': False, 'contains(pretty)': False, 'contains(neat)': False, 'contains(concept)': False, 'contains(executed)': False, 'contains(terribly)': False, 'contains(so)': False, 'contains(problems)': True, 'contains(well)': True, 'contains(its)': False, 'contains(main)': False, 'contains(problem)': False, 'contains(simply)': False, 'contains(too)': False, 'contains(jumbled)': False, 'contains(starts)': False, 'contains(off)': False, 'contains(normal)': False, 'contains(downshifts)': False, 'contains(fantasy)': False, 'contains(world)': True, 'contains(you)': True, 'contains(as)': True, 'contains(audience)': False, 'contains(member)': False, 'contains(no)': False, 'contains(going)': False, 'contains(dreams)': False, 'contains(characters)': False, 'contains(coming)': False, 'contains(back)': False, 'contains(from)': True, 'contains(dead)': False, 'contains(others)': True, 'contains(who)': True, 'contains(look)': True, 'contains(like)': True, 'contains(strange)': False, 'contains(apparitions)': False, 'contains(disappearances)': False, 'contains(looooot)': False, 'contains(chase)': True, 'contains(scenes)': False, 'contains(tons)': False, 'contains(weird)': False, 'contains(things)': True, 'contains(happen)': False, 'contains(most)': True, 'contains(not)': True, 'contains(explained)': False, 'contains(now)': False, 'contains(personally)': False, 'contains(don)': False, 'contains(trying)': False, 'contains(unravel)': False, 'contains(film)': False, 'contains(every)': False, 'contains(when)': True, 'contains(does)': False, 'contains(give)': False, 'contains(me)': True, 'contains(same)': True, 'contains(clue)': False, 'contains(over)': False, 'contains(again)': False, 'contains(kind)': True, 'contains(fed)': False, 'contains(up)': False, 'contains(after)': False, 'contains(while)': True, 'contains(biggest)': False, 'contains(obviously)': False, 'contains(got)': True, 'contains(big)': False, 'contains(secret)': False, 'contains(hide)': False, 'contains(seems)': False, 'contains(want)': False, 'contains(completely)': False, 'contains(until)': False, 'contains(final)': False, 'contains(five)': False, 'contains(minutes)': False, 'contains(do)': True, 'contains(make)': True, 'contains(entertaining)': False, 'contains(thrilling)': False, 'contains(or)': False, 'contains(engaging)': False, 'contains(meantime)': False, 'contains(really)': False, 'contains(sad)': False, 'contains(part)': False, 'contains(arrow)': False, 'contains(both)': False, 'contains(dig)': False, 'contains(flicks)': False, 'contains(we)': False, 'contains(actually)': True, 'contains(figured)': False, 'contains(by)': True, 'contains(half)': False, 'contains(way)': True, 'contains(point)': False, 'contains(strangeness)': False, 'contains(did)': False, 'contains(start)': True, 'contains(little)': True, 'contains(bit)': False, 'contains(sense)': False, 'contains(still)': False, 'contains(more)': False, 'contains(guess)': False, 'contains(bottom)': False, 'contains(line)': False, 'contains(movies)': True, 'contains(should)': False, 'contains(always)': False, 'contains(sure)': False, 'contains(before)': False, 'contains(given)': False, 'contains(password)': False, 'contains(enter)': False, 'contains(understanding)': False, 'contains(mean)': False, 'contains(showing)': False, 'contains(melissa)': False, 'contains(sagemiller)': False, 'contains(running)': False, 'contains(away)': False, 'contains(visions)': False, 'contains(about)': True, 'contains(20)': False, 'contains(throughout)': False, 'contains(plain)': False, 'contains(lazy)': False, 'contains(!)': True, 'contains(okay)': False, 'contains(people)': False, 'contains(chasing)': False, 'contains(know)': False, 'contains(need)': False, 'contains(how)': True, 'contains(giving)': False, 'contains(us)': True, 'contains(different)': False, 'contains(offering)': False, 'contains(further)': False, 'contains(insight)': False, 'contains(down)': False, 'contains(apparently)': False, 'contains(studio)': False, 'contains(took)': False, 'contains(director)': False, 'contains(chopped)': False, 'contains(themselves)': False, 'contains(shows)': False, 'contains(might)': False, 'contains(ve)': False, 'contains(been)': False, 'contains(decent)': False, 'contains(here)': True, 'contains(somewhere)': False, 'contains(suits)': False, 'contains(decided)': False, 'contains(turning)': False, 'contains(music)': False, 'contains(video)': False, 'contains(edge)': False, 'contains(would)': False, 'contains(actors)': False, 'contains(although)': False, 'contains(wes)': False, 'contains(bentley)': False, 'contains(seemed)': False, 'contains(be)': True, 'contains(playing)': True, 'contains(exact)': False, 'contains(character)': False, 'contains(he)': True, 'contains(american)': False, 'contains(beauty)': False, 'contains(only)': True, 'contains(new)': False, 'contains(neighborhood)': False, 'contains(my)': False, 'contains(kudos)': False, 'contains(holds)': False, 'contains(own)': True, 'contains(entire)': False, 'contains(feeling)': False, 'contains(unraveling)': False, 'contains(overall)': False, 'contains(doesn)': False, 'contains(stick)': False, 'contains(because)': False, 'contains(entertain)': False, 'contains(confusing)': False, 'contains(rarely)': False, 'contains(excites)': False, 'contains(feels)': False, 'contains(redundant)': False, 'contains(runtime)': False, 'contains(despite)': False, 'contains(ending)': False, 'contains(explanation)': False, 'contains(craziness)': False, 'contains(came)': False, 'contains(oh)': False, 'contains(horror)': False, 'contains(slasher)': False, 'contains(flick)': False, 'contains(packaged)': False, 'contains(someone)': False, 'contains(assuming)': False, 'contains(genre)': False, 'contains(hot)': False, 'contains(kids)': False, 'contains(also)': True, 'contains(wrapped)': False, 'contains(production)': False, 'contains(years)': False, 'contains(ago)': False, 'contains(sitting)': False, 'contains(shelves)': False, 'contains(ever)': True, 'contains(whatever)': False, 'contains(skip)': False, 'contains(where)': True, 'contains(joblo)': False, 'contains(nightmare)': False, 'contains(elm)': False, 'contains(street)': False, 'contains(3)': False, 'contains(7)': False, 'contains(/)': False, 'contains(10)': False, 'contains(blair)': False, 'contains(witch)': False, 'contains(2)': False, 'contains(crow)': False, 'contains(9)': False, 'contains(salvation)': False, 'contains(4)': False, 'contains(stir)': False, 'contains(echoes)': False, 'contains(8)': False, 'contains(happy)': False, 'contains(bastard)': False, 'contains(quick)': True, 'contains(damn)': False, 'contains(y2k)': False, 'contains(bug)': False, 'contains(starring)': False, 'contains(jamie)': False, 'contains(lee)': False, 'contains(curtis)': False, 'contains(another)': False, 'contains(baldwin)': False, 'contains(brother)': False, 'contains(william)': False, 'contains(time)': False, 'contains(story)': False, 'contains(regarding)': False, 'contains(crew)': False, 'contains(tugboat)': False, 'contains(comes)': False, 'contains(across)': False, 'contains(deserted)': False, 'contains(russian)': False, 'contains(tech)': False, 'contains(ship)': False, 'contains(kick)': False, 'contains(power)': False, 'contains(within)': False, 'contains(gore)': False, 'contains(bringing)': False, 'contains(few)': False, 'contains(action)': True, 'contains(sequences)': False, 'contains(virus)': False, 'contains(empty)': False, 'contains(flash)': False, 'contains(substance)': False, 'contains(why)': False, 'contains(was)': False, 'contains(middle)': False, 'contains(nowhere)': False, 'contains(origin)': False, 'contains(pink)': False, 'contains(flashy)': False, 'contains(thing)': False, 'contains(hit)': False, 'contains(mir)': False, 'contains(course)': True, 'contains(donald)': False, 'contains(sutherland)': False, 'contains(stumbling)': False, 'contains(around)': False, 'contains(drunkenly)': False, 'contains(hey)': False, 'contains(let)': False, 'contains(some)': False, 'contains(robots)': False, 'contains(acting)': False, 'contains(below)': False, 'contains(average)': False, 'contains(likes)': False, 'contains(re)': True, 'contains(likely)': False, 'contains(work)': False, 'contains(halloween)': False, 'contains(h20)': False, 'contains(wasted)': False, 'contains(real)': False, 'contains(star)': False, 'contains(stan)': False, 'contains(winston)': False, 'contains(robot)': False, 'contains(design)': False, 'contains(schnazzy)': False, 'contains(cgi)': False, 'contains(occasional)': False, 'contains(shot)': False, 'contains(picking)': False, 'contains(brain)': False, 'contains(if)': True, 'contains(body)': False, 'contains(parts)': False, 'contains(turn)': False, 'contains(otherwise)': False, 'contains(much)': False, 'contains(sunken)': False, 'contains(jaded)': False, 'contains(viewer)': False, 'contains(thankful)': False, 'contains(invention)': False, 'contains(timex)': False, 'contains(indiglo)': False, 'contains(based)': False, 'contains(late)': False, 'contains(1960)': False, 'contains(television)': False, 'contains(show)': False, 'contains(name)': False, 'contains(mod)': False, 'contains(squad)': False, 'contains(tells)': False, 'contains(tale)': False, 'contains(three)': False, 'contains(reformed)': False, 'contains(criminals)': False, 'contains(under)': False, 'contains(employ)': False, 'contains(police)': False, 'contains(undercover)': True, 'contains(however)': True, 'contains(wrong)': True, 'contains(evidence)': False, 'contains(gets)': True, 'contains(stolen)': False, 'contains(immediately)': False, 'contains(suspicion)': False, 'contains(ads)': False, 'contains(cuts)': False, 'contains(claire)': False, 'contains(dane)': False, 'contains(nice)': False, 'contains(hair)': False, 'contains(cute)': False, 'contains(outfits)': False, 'contains(car)': False, 'contains(chases)': False, 'contains(stuff)': False, 'contains(blowing)': False, 'contains(sounds)': False, 'contains(first)': False, 'contains(fifteen)': False, 'contains(quickly)': False, 'contains(becomes)': False, 'contains(apparent)': False, 'contains(certainly)': False, 'contains(slick)': False, 'contains(looking)': False, 'contains(complete)': False, 'contains(costumes)': False, 'contains(isn)': False, 'contains(enough)': False, 'contains(best)': True, 'contains(described)': False, 'contains(cross)': False, 'contains(between)': True, 'contains(hour)': False, 'contains(long)': False, 'contains(cop)': False, 'contains(stretched)': False, 'contains(span)': False, 'contains(single)': False, 'contains(clich)': False, 'contains(matter)': False, 'contains(elements)': False, 'contains(recycled)': False, 'contains(everything)': True, 'contains(already)': False, 'contains(seen)': False, 'contains(nothing)': False, 'contains(spectacular)': False, 'contains(sometimes)': False, 'contains(bordering)': False, 'contains(wooden)': False, 'contains(danes)': False, 'contains(omar)': False, 'contains(epps)': False, 'contains(deliver)': False, 'contains(their)': False, 'contains(lines)': False, 'contains(bored)': False, 'contains(transfers)': False, 'contains(onto)': False, 'contains(escape)': False, 'contains(relatively)': False, 'contains(unscathed)': False, 'contains(giovanni)': False, 'contains(ribisi)': False, 'contains(plays)': False, 'contains(resident)': False, 'contains(crazy)': False, 'contains(man)': False, 'contains(ultimately)': False, 'contains(being)': False, 'contains(worth)': True, 'contains(watching)': False, 'contains(unfortunately)': False, 'contains(save)': False, 'contains(convoluted)': False, 'contains(apart)': False, 'contains(occupying)': False, 'contains(screen)': True, 'contains(young)': False, 'contains(cast)': False, 'contains(clothes)': False, 'contains(hip)': False, 'contains(soundtrack)': False, 'contains(appears)': False, 'contains(geared)': False, 'contains(towards)': False, 'contains(teenage)': False, 'contains(mindset)': False, 'contains(r)': False, 'contains(rating)': False, 'contains(content)': False, 'contains(justify)': False, 'contains(juvenile)': False, 'contains(older)': False, 'contains(information)': False, 'contains(literally)': False, 'contains(spoon)': False, 'contains(hard)': False, 'contains(instead)': False, 'contains(telling)': False, 'contains(dialogue)': False, 'contains(poorly)': False, 'contains(written)': False, 'contains(extremely)': False, 'contains(predictable)': False, 'contains(progresses)': False, 'contains(won)': False, 'contains(care)': False, 'contains(heroes)': False, 'contains(any)': False, 'contains(jeopardy)': False, 'contains(ll)': False, 'contains(aren)': False, 'contains(basing)': False, 'contains(nobody)': False, 'contains(remembers)': False, 'contains(questionable)': False, 'contains(wisdom)': False, 'contains(especially)': True, 'contains(considers)': False, 'contains(target)': False, 'contains(fact)': False, 'contains(number)': False, 'contains(memorable)': False, 'contains(can)': False, 'contains(counted)': False, 'contains(hand)': False, 'contains(missing)': False, 'contains(finger)': False, 'contains(times)': False, 'contains(checked)': False, 'contains(six)': False, 'contains(clear)': False, 'contains(indication)': False, 'contains(them)': True, 'contains(than)': False, 'contains(cash)': False, 'contains(spending)': False, 'contains(dollar)': False, 'contains(judging)': False, 'contains(rash)': False, 'contains(awful)': False, 'contains(seeing)': True, 'contains(avoid)': False, 'contains(at)': False, 'contains(costs)': False, 'contains(quest)': False, 'contains(camelot)': False, 'contains(warner)': False, 'contains(bros)': False, 'contains(feature)': False, 'contains(length)': False, 'contains(fully)': False, 'contains(animated)': False, 'contains(steal)': False, 'contains(clout)': False, 'contains(disney)': False, 'contains(cartoon)': False, 'contains(empire)': False, 'contains(mouse)': False, 'contains(reason)': False, 'contains(worried)': False, 'contains(other)': True, 'contains(recent)': False, 'contains(challenger)': False, 'contains(throne)': False, 'contains(last)': False, 'contains(fall)': False, 'contains(promising)': False, 'contains(flawed)': False, 'contains(20th)': False, 'contains(century)': False, 'contains(fox)': False, 'contains(anastasia)': False, 'contains(hercules)': False, 'contains(lively)': False, 'contains(colorful)': False, 'contains(palate)': False, 'contains(had)': False, 'contains(beat)': False, 'contains(hands)': False, 'contains(crown)': False, 'contains(1997)': False, 'contains(piece)': False, 'contains(animation)': False, 'contains(year)': False, 'contains(contest)': False, 'contains(arrival)': False, 'contains(magic)': False, 'contains(kingdom)': False, 'contains(mediocre)': False, 'contains(--)': True, 'contains(d)': False, 'contains(pocahontas)': False, 'contains(those)': False, 'contains(keeping)': False, 'contains(score)': False, 'contains(nearly)': False, 'contains(dull)': False, 'contains(revolves)': False, 'contains(adventures)': False, 'contains(free)': False, 'contains(spirited)': False, 'contains(kayley)': False, 'contains(voiced)': False, 'contains(jessalyn)': False, 'contains(gilsig)': False, 'contains(early)': True, 'contains(daughter)': False, 'contains(belated)': False, 'contains(knight)': False, 'contains(king)': False, 'contains(arthur)': False, 'contains(round)': False, 'contains(table)': False, 'contains(dream)': False, 'contains(follow)': False, 'contains(father)': False, 'contains(footsteps)': False, 'contains(she)': True, 'contains(chance)': False, 'contains(evil)': False, 'contains(warlord)': False, 'contains(ruber)': False, 'contains(gary)': False, 'contains(oldman)': False, 'contains(ex)': False, 'contains(gone)': False, 'contains(steals)': False, 'contains(magical)': False, 'contains(sword)': False, 'contains(excalibur)': False, 'contains(accidentally)': False, 'contains(loses)': False, 'contains(dangerous)': True, 'contains(booby)': False, 'contains(trapped)': False, 'contains(forest)': False, 'contains(help)': True, 'contains(hunky)': False, 'contains(blind)': False, 'contains(timberland)': False, 'contains(dweller)': False, 'contains(garrett)': False, 'contains(carey)': False, 'contains(elwes)': False, 'contains(headed)': False, 'contains(dragon)': False, 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'contains(finds)': False, 'contains(usual)': False, 'contains(reaction)': False, 'contains(fluttered)': False, 'contains(paves)': False, 'contains(possible)': False, 'contains(pregnancy)': False, 'contains(birth)': False, 'contains(gag)': False, 'contains(book)': False, 'contains(friend)': False, 'contains(arnold)': True, 'contains(provides)': False, 'contains(cacophonous)': False, 'contains(funny)': True, 'contains(beats)': False, 'contains(costumed)': False, 'contains(arnie)': False, 'contains(dinosaur)': False, 'contains(draw)': False, 'contains(parallels)': False, 'contains(toy)': False, 'contains(store)': False, 'contains(jeff)': False, 'contains(goldblum)': False, 'contains(hid)': False, 'contains(dreadful)': False, 'contains(hideaway)': False, 'contains(artist)': False, 'contains(fear)': False, 'contains(simultaneous)': False, 'contains(longing)': False, 'contains(commitment)': False, 'contains(doctor)': False, 'contains(recently)': False, 'contains(switch)': False, 'contains(veterinary)': False, 'contains(medicine)': False, 'contains(obstetrics)': False, 'contains(joke)': False, 'contains(old)': False, 'contains(foreign)': False, 'contains(guy)': True, 'contains(mispronounces)': False, 'contains(stereotype)': False, 'contains(say)': False, 'contains(yakov)': False, 'contains(smirnov)': False, 'contains(favorite)': False, 'contains(vodka)': False, 'contains(hence)': False, 'contains(take)': False, 'contains(volvo)': False, 'contains(nasty)': False, 'contains(unamusing)': False, 'contains(heads)': False, 'contains(simultaneously)': False, 'contains(groan)': False, 'contains(failure)': False, 'contains(loud)': False, 'contains(failed)': False, 'contains(uninspired)': False, 'contains(lunacy)': False, 'contains(sunset)': False, 'contains(boulevard)': False, 'contains(arrest)': False, 'contains(please)': False, 'contains(caught)': False, 'contains(pants)': False, 'contains(bring)': False, 'contains(theaters)': False, 'contains(faces)': False, 'contains(90)': False, 'contains(forced)': False, 'contains(unauthentic)': False, 'contains(anyone)': False, 'contains(q)': False, 'contains(80)': False, 'contains(sorry)': False, 'contains(money)': False, 'contains(unfulfilled)': False, 'contains(desire)': False, 'contains(spend)': False, 'contains(bucks)': False, 'contains(call)': False, 'contains(road)': False, 'contains(trip)': False, 'contains(walking)': False, 'contains(wounded)': False, 'contains(stellan)': False, 'contains(skarsg)': False, 'contains(rd)': False, 'contains(convincingly)': False, 'contains(zombified)': False, 'contains(drunken)': False, 'contains(loser)': False, 'contains(difficult)': True, 'contains(smelly)': False, 'contains(boozed)': False, 'contains(reliable)': False, 'contains(swedish)': False, 'contains(adds)': False, 'contains(depth)': False, 'contains(significance)': False, 'contains(plodding)': False, 'contains(aberdeen)': False, 'contains(sentimental)': False, 'contains(painfully)': False, 'contains(mundane)': False, 'contains(european)': False, 'contains(playwright)': False, 'contains(august)': False, 'contains(strindberg)': False, 'contains(built)': False, 'contains(career)': False, 'contains(families)': False, 'contains(relationships)': False, 'contains(paralyzed)': False, 'contains(secrets)': False, 'contains(unable)': False, 'contains(express)': False, 'contains(longings)': False, 'contains(accurate)': False, 'contains(reflection)': False, 'contains(strives)': False, 'contains(focusing)': False, 'contains(pairing)': False, 'contains(alcoholic)': False, 'contains(tomas)': False, 'contains(alienated)': False, 'contains(openly)': False, 'contains(hostile)': False, 'contains(yuppie)': False, 'contains(kaisa)': False, 'contains(lena)': False, 'contains(headey)': False, 'contains(gossip)': False, 'contains(haven)': False, 'contains(spoken)': False, 'contains(wouldn)': False, 'contains(norway)': False, 'contains(scotland)': False, 'contains(automobile)': False, 'contains(charlotte)': False, 'contains(rampling)': False, 'contains(sand)': False, 'contains(rotting)': False, 'contains(hospital)': False, 'contains(bed)': False, 'contains(cancer)': False, 'contains(soap)': False, 'contains(opera)': False, 'contains(twist)': False, 'contains(days)': False, 'contains(live)': False, 'contains(blitzed)': False, 'contains(step)': False, 'contains(foot)': False, 'contains(plane)': False, 'contains(hits)': False, 'contains(open)': False, 'contains(loathing)': False, 'contains(each)': True, 'contains(periodic)': False, 'contains(stops)': True, 'contains(puke)': False, 'contains(dashboard)': False, 'contains(whenever)': False, 'contains(muttering)': False, 'contains(rotten)': False, 'contains(turned)': False, 'contains(sloshed)': False, 'contains(viewpoint)': False, 'contains(recognizes)': False, 'contains(apple)': False, 'contains(hasn)': False, 'contains(fallen)': False, 'contains(tree)': False, 'contains(nosebleeds)': False, 'contains(snorting)': False, 'contains(coke)': False, 'contains(sabotages)': False, 'contains(personal)': False, 'contains(indifference)': False, 'contains(restrain)': False, 'contains(vindictive)': False, 'contains(temper)': False, 'contains(ain)': False, 'contains(pair)': False, 'contains(true)': False, 'contains(notes)': False, 'contains(unspoken)': False, 'contains(familial)': False, 'contains(empathy)': False, 'contains(note)': False, 'contains(repetitively)': False, 'contains(bitchy)': False, 'contains(screenwriters)': False, 'contains(kristin)': False, 'contains(amundsen)': False, 'contains(hans)': False, 'contains(petter)': False, 'contains(moland)': False, 'contains(fabricate)': False, 'contains(series)': True, 'contains(contrivances)': False, 'contains(propel)': False, 'contains(forward)': False, 'contains(roving)': False, 'contains(hooligans)': False, 'contains(drunks)': False, 'contains(nosy)': False, 'contains(flat)': False, 'contains(tires)': False, 'contains(figure)': False, 'contains(schematic)': False, 'contains(convenient)': False, 'contains(narrative)': False, 'contains(reach)': False, 'contains(unveil)': False, 'contains(dark)': False, 'contains(past)': False, 'contains(simplistic)': False, 'contains(devices)': False, 'contains(trivialize)': False, 'contains(conflict)': False, 'contains(mainstays)': False, 'contains(wannabe)': False, 'contains(exists)': False, 'contains(purely)': False, 'contains(sake)': False, 'contains(weak)': False, 'contains(unimaginative)': False, 'contains(casting)': False, 'contains(thwarts)': False, 'contains(pivotal)': False, 'contains(role)': False, 'contains(were)': False, 'contains(stronger)': False, 'contains(actress)': False, 'contains(perhaps)': False, 'contains(coast)': True, 'contains(performances)': False, 'contains(moody)': False, 'contains(haunting)': False, 'contains(cinematography)': False, 'contains(rendering)': False, 'contains(pastoral)': False, 'contains(ghost)': False, 'contains(reference)': False, 'contains(certain)': False, 'contains(superior)': False, 'contains(indie)': False, 'contains(intentional)': False, 'contains(busy)': False, 'contains(using)': False, 'contains(furrowed)': False, 'contains(brow)': False, 'contains(convey)': False, 'contains(twitch)': False, 'contains(insouciance)': False, 'contains(paying)': False, 'contains(attention)': False, 'contains(maybe)': False, 'contains(doing)': False, 'contains(reveal)': False, 'contains(worthwhile)': False, 'contains(earlier)': False, 'contains(released)': False, 'contains(2001)': False, 'contains(jonathan)': False, 'contains(nossiter)': False, 'contains(captivating)': False, 'contains(wonders)': False, 'contains(disturbed)': False, 'contains(parental)': False, 'contains(figures)': False, 'contains(bound)': False, 'contains(ceremonial)': False, 'contains(wedlock)': False, 'contains(differences)': False, 'contains(presented)': False, 'contains(significant)': False, 'contains(luminous)': False, 'contains(diva)': False, 'contains(preening)': False, 'contains(static)': False, 'contains(solid)': False, 'contains(performance)': False, 'contains(pathetic)': False, 'contains(drunk)': False, 'contains(emote)': False, 'contains(besides)': False, 'contains(catatonic)': False, 'contains(sorrow)': False, 'contains(genuine)': False, 'contains(ferocity)': False, 'contains(sexually)': False, 'contains(charged)': False, 'contains(frisson)': False, 'contains(during)': False, 'contains(understated)': False, 'contains(confrontations)': False, 'contains(suggest)': False, 'contains(gray)': False, 'contains(zone)': False, 'contains(complications)': False, 'contains(accompany)': False, 'contains(torn)': False, 'contains(romance)': False, 'contains(stifled)': False, 'contains(curiosity)': False, 'contains(thoroughly)': False, 'contains(explores)': False, 'contains(neurotic)': False, 'contains(territory)': False, 'contains(delving)': False, 'contains(americanization)': False, 'contains(greece)': False, 'contains(mysticism)': False, 'contains(illusion)': False, 'contains(deflect)': False, 'contains(pain)': False, 'contains(overloaded)': False, 'contains(willing)': False, 'contains(come)': False, 'contains(traditional)': False, 'contains(ambitious)': False, 'contains(sleepwalk)': False, 'contains(rhythms)': False, 'contains(timing)': False, 'contains(driven)': False, 'contains(stories)': False, 'contains(complexities)': False, 'contains(depressing)': False, 'contains(answer)': False, 'contains(lawrence)': False, 'contains(kasdan)': False, 'contains(trite)': False, 'contains(useful)': False, 'contains(grand)': False, 'contains(canyon)': False, 'contains(steve)': False, 'contains(martin)': False, 'contains(mogul)': False, 'contains(pronounces)': False, 'contains(riddles)': False, 'contains(answered)': False, 'contains(advice)': False, 'contains(heart)': False, 'contains(french)': False, 'contains(sees)': True, 'contains(parents)': False, 'contains(tim)': False, 'contains(roth)': False, 'contains(oops)': False, 'contains(vows)': False, 'contains(taught)': False, 'contains(musketeer)': False, 'contains(dude)': False, 'contains(used)': True, 'contains(fourteen)': False, 'contains(arrgh)': False, 'contains(swish)': False, 'contains(zzzzzzz)': False, 'contains(original)': False, 'contains(lacks)': False, 'contains(energy)': False, 'contains(next)': False, 'contains(hmmmm)': False, 'contains(justin)': False, 'contains(chambers)': False, 'contains(basically)': False, 'contains(uncharismatic)': False, 'contains(version)': False, 'contains(chris)': False, 'contains(o)': False, 'contains(donnell)': False, 'contains(range)': False, 'contains(mena)': False, 'contains(suvari)': False, 'contains(thora)': False, 'contains(birch)': False, 'contains(dungeons)': False, 'contains(dragons)': False, 'contains(miscast)': False, 'contains(deliveries)': False, 'contains(piss)': False, 'contains(poor)': False, 'contains(ms)': False, 'contains(fault)': False, 'contains(definitely)': False, 'contains(higher)': False, 'contains(semi)': False, 'contains(saving)': False, 'contains(grace)': False, 'contains(wise)': False, 'contains(irrepressible)': False, 'contains(once)': True, 'contains(thousand)': False, 'contains(god)': False, 'contains(beg)': False, 'contains(agent)': False, 'contains(marketplace)': False, 'contains(modern)': False, 'contains(day)': True, 'contains(roles)': False, 'contains(romantic)': False, 'contains(gunk)': False, 'contains(alright)': False, 'contains(yeah)': False, 'contains(yikes)': False, 'contains(notches)': False, 'contains(fellas)': False, 'contains(blares)': False, 'contains(ear)': False, 'contains(accentuate)': False, 'contains(annoy)': False, 'contains(important)': False, 'contains(behind)': False, 'contains(recognize)': False, 'contains(epic)': False, 'contains(fluffy)': False, 'contains(rehashed)': False, 'contains(cake)': False, 'contains(created)': False, 'contains(shrewd)': False, 'contains(advantage)': False, 'contains(kung)': True, 'contains(fu)': True, 'contains(phenomenon)': False, 'contains(test)': False, 'contains(dudes)': False, 'contains(keep)': False, 'contains(reading)': False, 'contains(editing)': False, 'contains(shoddy)': False, 'contains(banal)': False, 'contains(stilted)': False, 'contains(plentiful)': False, 'contains(top)': True, 'contains(horse)': False, 'contains(carriage)': False, 'contains(stand)': False, 'contains(opponent)': False, 'contains(scampering)': False, 'contains(cut)': False, 'contains(mouseketeer)': False, 'contains(rope)': False, 'contains(tower)': False, 'contains(jumping)': False, 'contains(chords)': False, 'contains(hanging)': False, 'contains(says)': False, 'contains(14)': False, 'contains(shirt)': False, 'contains(strayed)': False, 'contains(championing)': False, 'contains(fun)': True, 'contains(stretches)': False, 'contains(atrocious)': False, 'contains(lake)': False, 'contains(reminded)': False, 'contains(school)': False, 'contains(cringe)': False, 'contains(musketeers)': False, 'contains(fat)': False, 'contains(raison)': False, 'contains(etre)': False, 'contains(numbers)': False, 'contains(hoping)': False, 'contains(packed)': False, 'contains(stuntwork)': False, 'contains(promoted)': False, 'contains(trailer)': False, 'contains(major)': False, 'contains(swashbuckling)': False, 'contains(beginning)': False, 'contains(finishes)': False, 'contains(juggling)': False, 'contains(ladders)': False, 'contains(ladder)': True, 'contains(definite)': False, 'contains(keeper)': False, 'contains(regurgitated)': False, 'contains(crap)': False, 'contains(tell)': False, 'contains(deneuve)': False, 'contains(placed)': False, 'contains(hullo)': False, 'contains(barely)': False, 'contains(ugh)': False, 'contains(small)': False, 'contains(annoyed)': False, 'contains(trash)': False, 'contains(gang)': False, 'contains(vow)': False, 'contains(stay)': False, 'contains(thank)': False, 'contains(outlaws)': False, 'contains(5)': False, 'contains(crouching)': False, 'contains(tiger)': False, 'contains(hidden)': False, 'contains(matrix)': False, 'contains(replacement)': False, 'contains(killers)': False, 'contains(6)': False, 'contains(romeo)': False, 'contains(die)': False, 'contains(shanghai)': False, 'contains(noon)': False, 'contains(remembered)': False, 'contains(dr)': False, 'contains(hannibal)': False, 'contains(lecter)': False, 'contains(michael)': False, 'contains(mann)': False, 'contains(forensics)': False, 'contains(thriller)': False, 'contains(manhunter)': False, 'contains(scottish)': False, 'contains(brian)': False, 'contains(cox)': False}\n"
     ]
    }
   ],
   "source": [
    "print(document_features(movie_reviews.words('pos/cv957_8737.txt')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "featuresets = [(document_features(d), c) for (d,c) in documents]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set, test_set = featuresets[100:], featuresets[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#featuresets[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83\n"
     ]
    }
   ],
   "source": [
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "        contains(justin) = True              neg : pos    =      9.5 : 1.0\n",
      " contains(unimaginative) = True              neg : pos    =      8.2 : 1.0\n",
      "    contains(schumacher) = True              neg : pos    =      7.3 : 1.0\n",
      "          contains(mena) = True              neg : pos    =      6.9 : 1.0\n",
      "     contains(atrocious) = True              neg : pos    =      6.9 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier.show_most_informative_features(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import brown\n",
    "suffix_fdist = nltk.FreqDist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prev-word': 'an', 'suffix(1)': 'n', 'suffix(2)': 'on', 'suffix(3)': 'ion'}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pos_features(sentence, i):\n",
    "    features = {\"suffix(1)\": sentence[i][-1:],\n",
    "                \"suffix(2)\": sentence[i][-2:],\n",
    "                \"suffix(3)\": sentence[i][-3:]}\n",
    "    if i == 0:\n",
    "        features[\"prev-word\"] = \"<START>\"\n",
    "    else:\n",
    "        features[\"prev-word\"] = sentence[i-1]\n",
    "    return features\n",
    "\n",
    "pos_features(brown.sents()[0], 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The',\n",
       " 'Fulton',\n",
       " 'County',\n",
       " 'Grand',\n",
       " 'Jury',\n",
       " 'said',\n",
       " 'Friday',\n",
       " 'an',\n",
       " 'investigation',\n",
       " 'of',\n",
       " \"Atlanta's\",\n",
       " 'recent',\n",
       " 'primary',\n",
       " 'election',\n",
       " 'produced',\n",
       " '``',\n",
       " 'no',\n",
       " 'evidence',\n",
       " \"''\",\n",
       " 'that',\n",
       " 'any',\n",
       " 'irregularities',\n",
       " 'took',\n",
       " 'place',\n",
       " '.']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown.sents()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tagged_sents = brown.tagged_sents(categories='news')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('The', 'AT'),\n",
       "  ('Fulton', 'NP-TL'),\n",
       "  ('County', 'NN-TL'),\n",
       "  ('Grand', 'JJ-TL'),\n",
       "  ('Jury', 'NN-TL'),\n",
       "  ('said', 'VBD'),\n",
       "  ('Friday', 'NR'),\n",
       "  ('an', 'AT'),\n",
       "  ('investigation', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  (\"Atlanta's\", 'NP$'),\n",
       "  ('recent', 'JJ'),\n",
       "  ('primary', 'NN'),\n",
       "  ('election', 'NN'),\n",
       "  ('produced', 'VBD'),\n",
       "  ('``', '``'),\n",
       "  ('no', 'AT'),\n",
       "  ('evidence', 'NN'),\n",
       "  (\"''\", \"''\"),\n",
       "  ('that', 'CS'),\n",
       "  ('any', 'DTI'),\n",
       "  ('irregularities', 'NNS'),\n",
       "  ('took', 'VBD'),\n",
       "  ('place', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('The', 'AT'),\n",
       "  ('jury', 'NN'),\n",
       "  ('further', 'RBR'),\n",
       "  ('said', 'VBD'),\n",
       "  ('in', 'IN'),\n",
       "  ('term-end', 'NN'),\n",
       "  ('presentments', 'NNS'),\n",
       "  ('that', 'CS'),\n",
       "  ('the', 'AT'),\n",
       "  ('City', 'NN-TL'),\n",
       "  ('Executive', 'JJ-TL'),\n",
       "  ('Committee', 'NN-TL'),\n",
       "  (',', ','),\n",
       "  ('which', 'WDT'),\n",
       "  ('had', 'HVD'),\n",
       "  ('over-all', 'JJ'),\n",
       "  ('charge', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  ('the', 'AT'),\n",
       "  ('election', 'NN'),\n",
       "  (',', ','),\n",
       "  ('``', '``'),\n",
       "  ('deserves', 'VBZ'),\n",
       "  ('the', 'AT'),\n",
       "  ('praise', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('thanks', 'NNS'),\n",
       "  ('of', 'IN'),\n",
       "  ('the', 'AT'),\n",
       "  ('City', 'NN-TL'),\n",
       "  ('of', 'IN-TL'),\n",
       "  ('Atlanta', 'NP-TL'),\n",
       "  (\"''\", \"''\"),\n",
       "  ('for', 'IN'),\n",
       "  ('the', 'AT'),\n",
       "  ('manner', 'NN'),\n",
       "  ('in', 'IN'),\n",
       "  ('which', 'WDT'),\n",
       "  ('the', 'AT'),\n",
       "  ('election', 'NN'),\n",
       "  ('was', 'BEDZ'),\n",
       "  ('conducted', 'VBN'),\n",
       "  ('.', '.')]]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged_sents[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Sequence Classification\n",
    "One sequence classification strategy, known as consecutive classification or greedy sequence classification, is to find the most likely class label for the first input, then to use that answer to help find the best label for the next input. The process can then be repeated until all of the inputs have been labeled. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pos_features(sentence, i, history):\n",
    "     features = {\"suffix(1)\": sentence[i][-1:],\n",
    "                 \"suffix(2)\": sentence[i][-2:],\n",
    "                 \"suffix(3)\": sentence[i][-3:]}\n",
    "     if i == 0:\n",
    "         features[\"prev-word\"] = \"<START>\"\n",
    "         features[\"prev-tag\"] = \"<START>\"\n",
    "     else:\n",
    "         features[\"prev-word\"] = sentence[i-1]\n",
    "         features[\"prev-tag\"] = history[i-1]\n",
    "     return features\n",
    "\n",
    "class ConsecutivePosTagger(nltk.TaggerI):\n",
    "\n",
    "    def __init__(self, train_sents):\n",
    "        train_set = []\n",
    "        for tagged_sent in train_sents:\n",
    "            untagged_sent = nltk.tag.untag(tagged_sent)\n",
    "            history = []\n",
    "            for i, (word, tag) in enumerate(tagged_sent):\n",
    "                featureset = pos_features(untagged_sent, i, history)\n",
    "                train_set.append( (featureset, tag) )\n",
    "                history.append(tag)\n",
    "        self.classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "\n",
    "    def tag(self, sentence):\n",
    "        history = []\n",
    "        for i, word in enumerate(sentence):\n",
    "            featureset = pos_features(sentence, i, history)\n",
    "            tag = self.classifier.classify(featureset)\n",
    "            history.append(tag)\n",
    "        return zip(sentence, history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7980528511821975\n"
     ]
    }
   ],
   "source": [
    "tagged_sents = brown.tagged_sents(categories='news')\n",
    "size = int(len(tagged_sents) * 0.1)\n",
    "train_sents, test_sents = tagged_sents[size:], tagged_sents[:size]\n",
    "tagger = ConsecutivePosTagger(train_sents)\n",
    "print(tagger.evaluate(test_sents))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recognizing Textual Entailment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rte_features(rtepair):\n",
    "    extractor = nltk.RTEFeatureExtractor(rtepair)\n",
    "    features = {}\n",
    "    features['word_overlap'] = len(extractor.overlap('word'))\n",
    "    features['word_hyp_extra'] = len(extractor.hyp_extra('word'))\n",
    "    features['ne_overlap'] = len(extractor.overlap('ne'))\n",
    "    features['ne_hyp_extra'] = len(extractor.hyp_extra('ne'))\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    " #rtepair = nltk.corpus.rte.pairs(['rte3_dev.xml'])[33]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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