Language Processing and Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chapter 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You should install NLTK, import NlTK and download the NLTK Book Collection at the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Introductory Examples for the NLTK Book ***\n",
      "Loading text1, ..., text9 and sent1, ..., sent9\n",
      "Type the name of the text or sentence to view it.\n",
      "Type: 'texts()' or 'sents()' to list the materials.\n",
      "text1: Moby Dick by Herman Melville 1851\n",
      "text2: Sense and Sensibility by Jane Austen 1811\n",
      "text3: The Book of Genesis\n",
      "text4: Inaugural Address Corpus\n",
      "text5: Chat Corpus\n",
      "text6: Monty Python and the Holy Grail\n",
      "text7: Wall Street Journal\n",
      "text8: Personals Corpus\n",
      "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n"
     ]
    }
   ],
   "source": [
    "from nltk.book import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['now',\n",
       " 'im',\n",
       " 'left',\n",
       " 'with',\n",
       " 'this',\n",
       " 'gay',\n",
       " 'name',\n",
       " ':P',\n",
       " 'PART',\n",
       " 'hey',\n",
       " 'everyone',\n",
       " 'ah',\n",
       " 'well',\n",
       " 'NICK',\n",
       " ':',\n",
       " 'U7',\n",
       " 'U7',\n",
       " 'is',\n",
       " 'a',\n",
       " 'gay',\n",
       " 'name',\n",
       " '.',\n",
       " '.',\n",
       " 'ACTION',\n",
       " 'gives',\n",
       " 'U121',\n",
       " 'a',\n",
       " 'golf',\n",
       " 'clap',\n",
       " '.',\n",
       " ':)',\n",
       " 'JOIN',\n",
       " 'hi',\n",
       " 'U59',\n",
       " '26',\n",
       " '/',\n",
       " 'm',\n",
       " '/',\n",
       " 'ky',\n",
       " 'women',\n",
       " 'that',\n",
       " 'are',\n",
       " 'nice',\n",
       " 'please',\n",
       " 'pm',\n",
       " 'me',\n",
       " 'JOIN',\n",
       " 'PART',\n",
       " 'there',\n",
       " 'ya',\n",
       " 'go',\n",
       " 'U7',\n",
       " 'do',\n",
       " \"n't\",\n",
       " 'golf',\n",
       " 'clap',\n",
       " 'me',\n",
       " '.',\n",
       " 'fuck',\n",
       " 'you',\n",
       " 'U121',\n",
       " ':@',\n",
       " 'whats',\n",
       " 'everyone',\n",
       " 'up',\n",
       " 'to',\n",
       " '?',\n",
       " 'PART',\n",
       " 'PART',\n",
       " 'i',\n",
       " \"'ll\",\n",
       " 'thunder',\n",
       " 'clap',\n",
       " 'your',\n",
       " 'ass',\n",
       " '.',\n",
       " 'PART',\n",
       " 'and',\n",
       " 'i',\n",
       " 'dont',\n",
       " 'even',\n",
       " 'know',\n",
       " 'what',\n",
       " 'that',\n",
       " 'means',\n",
       " '.',\n",
       " 'that',\n",
       " 'sounds',\n",
       " 'painful',\n",
       " 'any',\n",
       " 'ladis',\n",
       " 'wanna',\n",
       " 'chat',\n",
       " '?',\n",
       " '29',\n",
       " 'm',\n",
       " '26',\n",
       " '/',\n",
       " 'm',\n",
       " 'JOIN',\n",
       " 'my',\n",
       " 'cousin',\n",
       " 'drew',\n",
       " 'a',\n",
       " 'messed',\n",
       " 'up',\n",
       " 'pic',\n",
       " 'on',\n",
       " 'my',\n",
       " 'cast',\n",
       " 'PART',\n",
       " '24',\n",
       " '/',\n",
       " 'm',\n",
       " 'boo',\n",
       " '.',\n",
       " '26',\n",
       " '/',\n",
       " 'm',\n",
       " 'and',\n",
       " 'sexy',\n",
       " 'lol',\n",
       " 'U115',\n",
       " 'boo',\n",
       " '.',\n",
       " 'JOIN',\n",
       " 'PART',\n",
       " 'he',\n",
       " 'drew',\n",
       " 'a',\n",
       " 'girl',\n",
       " 'with',\n",
       " 'legs',\n",
       " 'spread',\n",
       " 'boo',\n",
       " '.',\n",
       " 'hope',\n",
       " 'he',\n",
       " 'didnt',\n",
       " 'draw',\n",
       " 'a',\n",
       " 'penis',\n",
       " 'PART',\n",
       " 'ewwwww',\n",
       " 'lol',\n",
       " '&',\n",
       " 'a',\n",
       " 'head',\n",
       " 'between',\n",
       " 'her',\n",
       " 'legs',\n",
       " 'JOIN',\n",
       " 'JOIN',\n",
       " 'sounds',\n",
       " 'good',\n",
       " 'to',\n",
       " 'me',\n",
       " '.',\n",
       " 'r',\n",
       " 'u',\n",
       " 'serious',\n",
       " 'JOIN',\n",
       " 'PART',\n",
       " 'I',\n",
       " \"'ll\",\n",
       " 'take',\n",
       " 'one',\n",
       " ',',\n",
       " 'please',\n",
       " '.',\n",
       " '&',\n",
       " 'i',\n",
       " 'have',\n",
       " 'to',\n",
       " 'go',\n",
       " 'to',\n",
       " 'the',\n",
       " 'docs',\n",
       " 'tomorrow',\n",
       " 'ya',\n",
       " 'man',\n",
       " 'I',\n",
       " 'am',\n",
       " 'too',\n",
       " '..',\n",
       " 'Connected',\n",
       " 'to',\n",
       " '...',\n",
       " 'Slip',\n",
       " 'away',\n",
       " '...',\n",
       " 'Fade',\n",
       " 'away',\n",
       " '...',\n",
       " 'Days',\n",
       " 'away',\n",
       " 'I',\n",
       " '...',\n",
       " 'Still',\n",
       " 'feel',\n",
       " 'you',\n",
       " '...',\n",
       " 'Touching',\n",
       " 'me',\n",
       " '...',\n",
       " 'Changing',\n",
       " 'me',\n",
       " '...',\n",
       " 'Considerably',\n",
       " 'killing',\n",
       " 'me',\n",
       " '...',\n",
       " 'heeeey',\n",
       " '!',\n",
       " 'do',\n",
       " \"n't\",\n",
       " 'you',\n",
       " 'have',\n",
       " 'a',\n",
       " 'sharpie',\n",
       " '?',\n",
       " '26',\n",
       " '/',\n",
       " 'm',\n",
       " 'you',\n",
       " \"'re\",\n",
       " 'back',\n",
       " 'U115',\n",
       " 'U129',\n",
       " 'yep',\n",
       " 'U115',\n",
       " 'Any',\n",
       " 'ladies',\n",
       " 'wanna',\n",
       " 'chat',\n",
       " 'with',\n",
       " '24',\n",
       " '/',\n",
       " 'm',\n",
       " 'hurry',\n",
       " 'ladies',\n",
       " 'PART',\n",
       " 'JOIN',\n",
       " 'JOIN',\n",
       " 'not',\n",
       " 'fast',\n",
       " 'enough',\n",
       " 'U116',\n",
       " 'a',\n",
       " 'bowl',\n",
       " 'i',\n",
       " 'got',\n",
       " 'a',\n",
       " 'blunt',\n",
       " 'an',\n",
       " 'a',\n",
       " 'bong',\n",
       " '......',\n",
       " 'lol',\n",
       " 'JOIN',\n",
       " 'well',\n",
       " ',',\n",
       " 'glad',\n",
       " 'it',\n",
       " 'worked',\n",
       " 'out',\n",
       " 'my',\n",
       " 'chair',\n",
       " 'is',\n",
       " 'too',\n",
       " 'hard',\n",
       " '.',\n",
       " 'Anyone',\n",
       " 'from',\n",
       " 'Tennessee',\n",
       " 'in',\n",
       " 'here',\n",
       " '?',\n",
       " 'hey',\n",
       " 'ladies',\n",
       " 'as',\n",
       " 'am',\n",
       " 'i',\n",
       " 'is',\n",
       " 'U68',\n",
       " 'back',\n",
       " 'yet',\n",
       " 'PART',\n",
       " 'hey',\n",
       " 'PART',\n",
       " 'JOIN',\n",
       " 'U121',\n",
       " 'is',\n",
       " 'missing',\n",
       " 'a',\n",
       " 'B',\n",
       " 'in',\n",
       " 'her',\n",
       " 'name',\n",
       " 'and',\n",
       " 'i',\n",
       " 'do',\n",
       " \"n't\",\n",
       " 'complain',\n",
       " 'about',\n",
       " 'things',\n",
       " 'being',\n",
       " 'hard',\n",
       " 'very',\n",
       " 'often',\n",
       " '.',\n",
       " 'ok',\n",
       " 'yes',\n",
       " 'U30',\n",
       " 'fire',\n",
       " 'it',\n",
       " 'up',\n",
       " 'Any',\n",
       " 'women',\n",
       " 'from',\n",
       " 'Nashville',\n",
       " 'in',\n",
       " 'here',\n",
       " '?',\n",
       " 'JOIN',\n",
       " 'PART',\n",
       " 'and',\n",
       " 'an',\n",
       " 'an',\n",
       " '\"',\n",
       " 'itch',\n",
       " '\"',\n",
       " 'JOIN',\n",
       " 'yo',\n",
       " ',',\n",
       " 'U133',\n",
       " 'or',\n",
       " 'a',\n",
       " '\"',\n",
       " 'ogan',\n",
       " '\"',\n",
       " 'are',\n",
       " 'you',\n",
       " 'a',\n",
       " 'male',\n",
       " '?',\n",
       " 'JOIN',\n",
       " 'JOIN',\n",
       " 'show',\n",
       " 'will',\n",
       " 'let',\n",
       " \"'s\",\n",
       " 'talk',\n",
       " '.',\n",
       " 'PART',\n",
       " ':)',\n",
       " 'haha',\n",
       " 'brb',\n",
       " 'opps',\n",
       " 'JOIN',\n",
       " 'PART',\n",
       " 'sho',\n",
       " '*',\n",
       " '.',\n",
       " 'ACTION',\n",
       " 'keeps',\n",
       " 'U115',\n",
       " 's',\n",
       " 'place',\n",
       " 'nice',\n",
       " 'and',\n",
       " 'warm',\n",
       " '.',\n",
       " 'hey',\n",
       " 'any',\n",
       " 'guys',\n",
       " 'with',\n",
       " 'cams',\n",
       " 'wanna',\n",
       " 'play',\n",
       " '?',\n",
       " '.',\n",
       " 'ACTION',\n",
       " 'sits',\n",
       " 'on',\n",
       " 'U68',\n",
       " \"'s\",\n",
       " 'lap',\n",
       " '.',\n",
       " 'JOIN',\n",
       " 'JOIN',\n",
       " 'any',\n",
       " 'guyz',\n",
       " 'wanna',\n",
       " 'chat',\n",
       " 'hi',\n",
       " 'there',\n",
       " 'boo',\n",
       " ',',\n",
       " 'it',\n",
       " \"'s\",\n",
       " 'a',\n",
       " 'female',\n",
       " '.',\n",
       " 'hey',\n",
       " 'U126',\n",
       " 'PART',\n",
       " 'i',\n",
       " 'wonna',\n",
       " 'chat',\n",
       " 'PART',\n",
       " '24',\n",
       " 'f',\n",
       " 'nc',\n",
       " 'single',\n",
       " 'mom',\n",
       " 'where',\n",
       " 'did',\n",
       " 'everyone',\n",
       " 'gooo',\n",
       " '?',\n",
       " 'sure',\n",
       " 'U126',\n",
       " 'JOIN',\n",
       " 'what',\n",
       " 'did',\n",
       " 'you',\n",
       " 'but',\n",
       " 'on',\n",
       " 'e-bay',\n",
       " 'i',\n",
       " 'feel',\n",
       " 'like',\n",
       " 'im',\n",
       " 'in',\n",
       " 'the',\n",
       " 'wrong',\n",
       " 'room',\n",
       " 'yeee',\n",
       " 'haw',\n",
       " 'U30',\n",
       " 'im',\n",
       " 'considering',\n",
       " 'changing',\n",
       " 'my',\n",
       " 'nickname',\n",
       " 'to',\n",
       " '\"',\n",
       " 'ihavehotnips',\n",
       " '\"',\n",
       " 'JOIN',\n",
       " 'i',\n",
       " 'do',\n",
       " \"n't\",\n",
       " 'want',\n",
       " 'hot',\n",
       " 'pics',\n",
       " 'of',\n",
       " 'a',\n",
       " 'female',\n",
       " ',',\n",
       " 'I',\n",
       " 'can',\n",
       " 'look',\n",
       " 'in',\n",
       " 'a',\n",
       " 'mirror',\n",
       " '.',\n",
       " 'hi',\n",
       " 'U64',\n",
       " 'wb',\n",
       " 'U139',\n",
       " 'u',\n",
       " 'should',\n",
       " 'U44',\n",
       " ':)',\n",
       " 'PART',\n",
       " 'PART',\n",
       " 'JOIN',\n",
       " 'single',\n",
       " 'dad',\n",
       " 'here',\n",
       " 'JOIN',\n",
       " 'ty',\n",
       " 'U68',\n",
       " 'PART',\n",
       " 'JOIN',\n",
       " 'Hi',\n",
       " 'U139',\n",
       " 'PART',\n",
       " 'JOIN',\n",
       " 'hi',\n",
       " 'U138',\n",
       " 'HAHAHA',\n",
       " 'yw',\n",
       " 'U139',\n",
       " 'you',\n",
       " 'should',\n",
       " 'make',\n",
       " 'it',\n",
       " \"'\",\n",
       " 'iamahotnip',\n",
       " \"'\",\n",
       " ',',\n",
       " 'U44',\n",
       " 'alright',\n",
       " 'hi',\n",
       " 'U139',\n",
       " '.',\n",
       " 'you',\n",
       " \"'re\",\n",
       " 'fucking',\n",
       " 'hot',\n",
       " '.',\n",
       " 'i',\n",
       " 'thought',\n",
       " 'of',\n",
       " 'that',\n",
       " '!',\n",
       " 'hi',\n",
       " 'U126',\n",
       " ',',\n",
       " 'its',\n",
       " 'so',\n",
       " 'late',\n",
       " 'lmao',\n",
       " 'ahah',\n",
       " '\"',\n",
       " 'iamahotniplickme',\n",
       " '\"',\n",
       " 'PART',\n",
       " 'Hi',\n",
       " 'U121',\n",
       " 'in',\n",
       " 'ny',\n",
       " '.',\n",
       " 'ACTION',\n",
       " 'would',\n",
       " 'lol',\n",
       " '@',\n",
       " 'U121',\n",
       " '. . .',\n",
       " 'but',\n",
       " 'appearently',\n",
       " 'she',\n",
       " 'does',\n",
       " 'not',\n",
       " 'like',\n",
       " 'that',\n",
       " '.',\n",
       " 'cya',\n",
       " 'later',\n",
       " 'guys',\n",
       " 'single',\n",
       " 'white',\n",
       " 'male',\n",
       " '26',\n",
       " 'ca',\n",
       " \"n't\",\n",
       " 'sleep',\n",
       " 'huh',\n",
       " '?',\n",
       " 'JOIN',\n",
       " 'hi',\n",
       " 'U139',\n",
       " 'bye',\n",
       " 'U20',\n",
       " 'well',\n",
       " 'U68',\n",
       " 'what',\n",
       " 'did',\n",
       " 'u',\n",
       " 'buy',\n",
       " 'yeah',\n",
       " 'i',\n",
       " 'know',\n",
       " ':)',\n",
       " 'U136',\n",
       " '..',\n",
       " 'get',\n",
       " 'the',\n",
       " 'hell',\n",
       " 'outta',\n",
       " 'my',\n",
       " 'freaking',\n",
       " 'PM',\n",
       " 'box',\n",
       " '..',\n",
       " 'Im',\n",
       " 'with',\n",
       " 'my',\n",
       " 'fiance',\n",
       " '!!!!!!!!!!!!!!!!',\n",
       " 'answers',\n",
       " 'for',\n",
       " 'U139',\n",
       " '...',\n",
       " 'hi',\n",
       " 'U101',\n",
       " ';)',\n",
       " 'I',\n",
       " 'like',\n",
       " 'it',\n",
       " 'when',\n",
       " 'you',\n",
       " 'do',\n",
       " 'it',\n",
       " ',',\n",
       " 'U83',\n",
       " 'iamahotnipwithpics',\n",
       " 'U20',\n",
       " 'go',\n",
       " 'plan',\n",
       " 'the',\n",
       " 'wedding',\n",
       " '!',\n",
       " ':P',\n",
       " 'first',\n",
       " 'warning',\n",
       " '!!!!!',\n",
       " 'PART',\n",
       " 'JOIN',\n",
       " 'uh',\n",
       " 'huh',\n",
       " 'A',\n",
       " 'gold',\n",
       " 'jeep',\n",
       " 'charm',\n",
       " 'for',\n",
       " 'my',\n",
       " 'necklace',\n",
       " 'U30',\n",
       " 'make',\n",
       " 'sure',\n",
       " 'u',\n",
       " 'buy',\n",
       " 'a',\n",
       " 'nice',\n",
       " 'ring',\n",
       " 'for',\n",
       " 'U6',\n",
       " 'lol',\n",
       " 'U7',\n",
       " 'Hi',\n",
       " 'U115',\n",
       " '.',\n",
       " 'ACTION',\n",
       " 'isnt',\n",
       " 'falling',\n",
       " 'for',\n",
       " 'that',\n",
       " 'one',\n",
       " 'again',\n",
       " '.',\n",
       " 'U136',\n",
       " '..',\n",
       " 'get',\n",
       " 'the',\n",
       " 'hell',\n",
       " 'in',\n",
       " 'my',\n",
       " 'freaking',\n",
       " 'PM',\n",
       " 'box',\n",
       " '..',\n",
       " 'I',\n",
       " 'have',\n",
       " 'no',\n",
       " 'fiance',\n",
       " '!!!!!!!',\n",
       " 'right',\n",
       " 'on',\n",
       " 'i',\n",
       " 'have',\n",
       " 'one',\n",
       " 'already',\n",
       " 'U7',\n",
       " '...',\n",
       " 'yayayayayyy',\n",
       " '!!',\n",
       " 'OOooOO',\n",
       " ':)',\n",
       " 'how',\n",
       " 'you',\n",
       " 'doin',\n",
       " 'U139',\n",
       " 'lmaoo',\n",
       " 'U121',\n",
       " \"'\",\n",
       " 'iamahotnipwithhotnippics',\n",
       " \"'\",\n",
       " 'how',\n",
       " 'many',\n",
       " 'kts',\n",
       " 'any',\n",
       " 'single',\n",
       " 'white',\n",
       " 'females',\n",
       " '?',\n",
       " 'off',\n",
       " 'off',\n",
       " '?',\n",
       " 'really',\n",
       " '?',\n",
       " 'Just',\n",
       " 'fine',\n",
       " 'thanks',\n",
       " ',',\n",
       " 'how',\n",
       " 'are',\n",
       " 'you',\n",
       " '?',\n",
       " 'that',\n",
       " 'sounds',\n",
       " 'freakishly',\n",
       " 'like',\n",
       " 'dr',\n",
       " 'seuss',\n",
       " 'wow',\n",
       " '.',\n",
       " 'twice',\n",
       " ',',\n",
       " 'I',\n",
       " \"'m\",\n",
       " 'impressed',\n",
       " '.',\n",
       " 'PART',\n",
       " 'hiya',\n",
       " 'room',\n",
       " 'lmao',\n",
       " '!!!',\n",
       " 'im',\n",
       " 'doin',\n",
       " 'alright',\n",
       " 'thanks',\n",
       " 'omg',\n",
       " 'Finger',\n",
       " '..',\n",
       " 'Deep',\n",
       " 'within',\n",
       " 'the',\n",
       " 'borderline',\n",
       " '..',\n",
       " 'Show',\n",
       " 'me',\n",
       " 'that',\n",
       " 'you',\n",
       " 'love',\n",
       " 'me',\n",
       " ',',\n",
       " 'and',\n",
       " 'that',\n",
       " 'we',\n",
       " 'belong',\n",
       " 'together',\n",
       " '...',\n",
       " 'Relax',\n",
       " '..',\n",
       " 'Turn',\n",
       " 'around',\n",
       " '..',\n",
       " 'And',\n",
       " 'take',\n",
       " 'my',\n",
       " '..',\n",
       " 'Hand',\n",
       " '..',\n",
       " 'I',\n",
       " 'just',\n",
       " 'laughed',\n",
       " 'didnt',\n",
       " 'even',\n",
       " 'look',\n",
       " 'hang',\n",
       " 'on',\n",
       " 'finger',\n",
       " '?',\n",
       " 'yay',\n",
       " '!',\n",
       " 'PART',\n",
       " 'that',\n",
       " \"'s\",\n",
       " 'such',\n",
       " 'a',\n",
       " 'DIRTY',\n",
       " 'word',\n",
       " '.',\n",
       " 'oh',\n",
       " 'its',\n",
       " '14',\n",
       " 'k',\n",
       " 'U141',\n",
       " '...',\n",
       " 'get',\n",
       " 'outta',\n",
       " 'my',\n",
       " 'PM',\n",
       " 'Box',\n",
       " '..',\n",
       " 'didnt',\n",
       " 'ya',\n",
       " 'hear',\n",
       " '!!!!',\n",
       " 'PART',\n",
       " 'JOIN',\n",
       " 'geeshhh',\n",
       " 'lol',\n",
       " 'U6',\n",
       " 'PART',\n",
       " 'hes',\n",
       " 'deaf',\n",
       " 'ppl',\n",
       " 'here',\n",
       " 'dont',\n",
       " 'get',\n",
       " 'it',\n",
       " 'U6',\n",
       " ',',\n",
       " '!',\n",
       " 'hug',\n",
       " 'U115',\n",
       " '.',\n",
       " 'ACTION',\n",
       " 'watches',\n",
       " 'U6',\n",
       " 'hug',\n",
       " 'the',\n",
       " \"stuffin'\",\n",
       " 'outta',\n",
       " 'U115',\n",
       " '....',\n",
       " 'you',\n",
       " 'can',\n",
       " 'forward',\n",
       " 'the',\n",
       " 'pervs',\n",
       " 'to',\n",
       " 'me',\n",
       " '.',\n",
       " '.',\n",
       " 'ACTION',\n",
       " 'hugs',\n",
       " 'U6',\n",
       " '.',\n",
       " 'any',\n",
       " 'girls',\n",
       " 'wanna',\n",
       " 'chat',\n",
       " 'with',\n",
       " '24',\n",
       " '/',\n",
       " 'm',\n",
       " 'please',\n",
       " 'behave',\n",
       " 'baby',\n",
       " 'boy',\n",
       " '..',\n",
       " 'I',\n",
       " 'gottsa',\n",
       " 'go',\n",
       " 'now',\n",
       " 'Only',\n",
       " 'for',\n",
       " 'the',\n",
       " 'dirty',\n",
       " 'minded',\n",
       " ',',\n",
       " 'U121',\n",
       " '.',\n",
       " 'ima',\n",
       " 'behave',\n",
       " 'yes',\n",
       " 'U121',\n",
       " '??',\n",
       " 'PART',\n",
       " 'aww',\n",
       " 'U6',\n",
       " 'have',\n",
       " 'fun',\n",
       " 'forwads',\n",
       " 'away',\n",
       " 'from',\n",
       " 'U121',\n",
       " 'i',\n",
       " 'already',\n",
       " 'wrote',\n",
       " 'what',\n",
       " 'i',\n",
       " 'wanted',\n",
       " 'you',\n",
       " 'to',\n",
       " 'read',\n",
       " '.',\n",
       " 'theres',\n",
       " 'nobody',\n",
       " 'here',\n",
       " 'i',\n",
       " 'wanna',\n",
       " 'misbeahve',\n",
       " 'with',\n",
       " 'lol',\n",
       " 'JOIN',\n",
       " 'so',\n",
       " 'read',\n",
       " 'it',\n",
       " '.',\n",
       " 'thanks',\n",
       " 'U7',\n",
       " '..',\n",
       " 'Im',\n",
       " 'happy',\n",
       " 'to',\n",
       " 'have',\n",
       " 'my',\n",
       " 'fiance',\n",
       " 'here',\n",
       " '!!',\n",
       " 'forwads',\n",
       " '?',\n",
       " 'JOIN',\n",
       " '22',\n",
       " '/',\n",
       " 'm',\n",
       " '/',\n",
       " 'wa',\n",
       " 'any',\n",
       " 'ladies',\n",
       " 'want',\n",
       " 'to',\n",
       " 'chat',\n",
       " 'can',\n",
       " 'i',\n",
       " 'talk',\n",
       " 'to',\n",
       " 'him',\n",
       " '!!',\n",
       " 'lol',\n",
       " 'U121',\n",
       " '!!!',\n",
       " 'forwards',\n",
       " 'too',\n",
       " 'lol',\n",
       " 'JOIN',\n",
       " 'ALL',\n",
       " 'PErvs',\n",
       " '...',\n",
       " 'redirect',\n",
       " 'to',\n",
       " 'U121',\n",
       " \"'s\",\n",
       " 'PM',\n",
       " 'VBox',\n",
       " '!!!',\n",
       " 'PART',\n",
       " 'I',\n",
       " \"'ll\",\n",
       " 'take',\n",
       " 'all',\n",
       " 'that',\n",
       " 'I',\n",
       " 'can',\n",
       " 'get',\n",
       " '.',\n",
       " 'JOIN',\n",
       " 'PART',\n",
       " 'who',\n",
       " 'is',\n",
       " 'he',\n",
       " 'U6',\n",
       " '?',\n",
       " 'i',\n",
       " 'hate',\n",
       " 'it',\n",
       " 'when',\n",
       " 'my',\n",
       " 'devotion',\n",
       " 'to',\n",
       " 'christianity',\n",
       " 'is',\n",
       " 'questioned',\n",
       " '.',\n",
       " 'U121',\n",
       " 'who',\n",
       " 'honey',\n",
       " '??',\n",
       " 'Depends',\n",
       " 'Hi',\n",
       " 'all',\n",
       " '31',\n",
       " '/',\n",
       " 'm',\n",
       " '/',\n",
       " 'CA',\n",
       " 'U115',\n",
       " 'is',\n",
       " 'having',\n",
       " 'my',\n",
       " 'babies',\n",
       " 'U20',\n",
       " 'yep',\n",
       " ...]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[w for w in text5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 1.1 Find the collocations in text2 from NLTK Book Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colonel Brandon; Sir John; Lady Middleton; Miss Dashwood; every thing; thousand pounds; dare say; Miss Steeles; said Elinor; Miss Steele; every body; John Dashwood; great deal; Harley Street; Berkeley Street; Miss Dashwoods; young man; Combe Magna; every day; next morning\n"
     ]
    }
   ],
   "source": [
    "print('; '.join(text2.collocation_list()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 1.2 Review the discussion of conditionals in 4. Find all words in the Chat Corpus (text5) ending with the letter l. Show them in alphabetical order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"'ll\",\n",
       " 'Cool',\n",
       " 'Ctrl',\n",
       " 'Daniel',\n",
       " 'Devil',\n",
       " 'Evil',\n",
       " 'Girl',\n",
       " 'Hail',\n",
       " 'Hill',\n",
       " 'Kewl',\n",
       " 'LOl',\n",
       " 'Lil',\n",
       " 'Lol',\n",
       " 'Paul',\n",
       " 'Paxil',\n",
       " 'PayPal',\n",
       " 'Pearl',\n",
       " 'Phil',\n",
       " 'Rascal',\n",
       " 'Rofl',\n",
       " 'School',\n",
       " 'Steel',\n",
       " 'Still',\n",
       " 'Tell',\n",
       " 'Troll',\n",
       " 'Until',\n",
       " 'VVil',\n",
       " 'Well',\n",
       " 'Will',\n",
       " 'al',\n",
       " 'alcohol',\n",
       " 'all',\n",
       " 'anal',\n",
       " 'angel',\n",
       " 'animal',\n",
       " 'anygirl',\n",
       " 'april',\n",
       " 'asl',\n",
       " 'astral',\n",
       " 'atl',\n",
       " 'bacl',\n",
       " 'bagel',\n",
       " 'ball',\n",
       " 'barrel',\n",
       " 'bbl',\n",
       " 'beautiful',\n",
       " 'bell',\n",
       " 'betrayal',\n",
       " 'bisexual',\n",
       " 'bowl',\n",
       " 'bull',\n",
       " 'cal',\n",
       " 'call',\n",
       " 'camel',\n",
       " 'careful',\n",
       " 'cell',\n",
       " 'channel',\n",
       " 'coastal',\n",
       " 'control',\n",
       " 'controll',\n",
       " 'cool',\n",
       " 'coudl',\n",
       " 'daniel',\n",
       " 'deal',\n",
       " 'denial',\n",
       " 'denzel',\n",
       " 'devil',\n",
       " 'distrustful',\n",
       " 'dl',\n",
       " 'doll',\n",
       " 'e-mail',\n",
       " 'el',\n",
       " 'email',\n",
       " 'equal',\n",
       " 'evil',\n",
       " 'fall',\n",
       " 'fantastical',\n",
       " 'feel',\n",
       " 'financial',\n",
       " 'fl',\n",
       " 'fongul',\n",
       " 'fool',\n",
       " 'football',\n",
       " 'full',\n",
       " 'gal',\n",
       " 'genital',\n",
       " 'girl',\n",
       " 'grrl',\n",
       " 'gurrrrl',\n",
       " 'hail',\n",
       " 'hall',\n",
       " 'heal',\n",
       " 'hel',\n",
       " 'hell',\n",
       " 'helpful',\n",
       " 'hill',\n",
       " 'homosexual',\n",
       " 'howl',\n",
       " 'il',\n",
       " 'ill',\n",
       " 'individual',\n",
       " 'irl',\n",
       " 'jail',\n",
       " 'kewl',\n",
       " 'kill',\n",
       " 'kool',\n",
       " 'l',\n",
       " 'label',\n",
       " 'lawl',\n",
       " 'level',\n",
       " 'lil',\n",
       " 'll',\n",
       " 'local',\n",
       " 'lol',\n",
       " 'lolol',\n",
       " 'lolololll',\n",
       " 'lool',\n",
       " 'lysol',\n",
       " 'magical',\n",
       " 'maniacal',\n",
       " 'metal',\n",
       " 'moral',\n",
       " 'national',\n",
       " 'natural',\n",
       " 'novel',\n",
       " 'oatmeal',\n",
       " 'oatmel',\n",
       " 'occasional',\n",
       " 'official',\n",
       " 'oil',\n",
       " 'ol',\n",
       " 'original',\n",
       " 'painful',\n",
       " 'pal',\n",
       " 'partial',\n",
       " 'paxil',\n",
       " 'paypal',\n",
       " 'peel',\n",
       " 'personal',\n",
       " 'phil',\n",
       " 'physical',\n",
       " 'pmsl',\n",
       " 'political',\n",
       " 'poll',\n",
       " 'pool',\n",
       " 'ppl',\n",
       " 'ppolitical',\n",
       " 'pull',\n",
       " 'quarrel',\n",
       " 'real',\n",
       " 'recall',\n",
       " 'refill',\n",
       " 'reschedual',\n",
       " 'rofl',\n",
       " 'roll',\n",
       " 'school',\n",
       " 'scroll',\n",
       " 'sell',\n",
       " 'sexual',\n",
       " 'shall',\n",
       " 'shell',\n",
       " 'shopgirl',\n",
       " 'small',\n",
       " 'smell',\n",
       " 'soul',\n",
       " 'special',\n",
       " 'spell',\n",
       " 'steal',\n",
       " 'still',\n",
       " 'tail',\n",
       " 'tall',\n",
       " 'technical',\n",
       " 'tell',\n",
       " 'thrill',\n",
       " 'til',\n",
       " 'till',\n",
       " 'total',\n",
       " 'towel',\n",
       " 'troll',\n",
       " 'twoel',\n",
       " 'unreal',\n",
       " 'until',\n",
       " 'unusual',\n",
       " 'url',\n",
       " 'use full',\n",
       " 'usual',\n",
       " 'vinyl',\n",
       " 'virginal',\n",
       " 'visual',\n",
       " 'wall',\n",
       " 'weasel',\n",
       " 'wel',\n",
       " 'well',\n",
       " 'welll',\n",
       " 'wheel',\n",
       " 'will',\n",
       " 'withdrawal',\n",
       " 'withdrawl',\n",
       " 'wonderful',\n",
       " 'worl',\n",
       " 'yall',\n",
       " 'yell',\n",
       " 'yoll']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(w for w in set(text5) if w.endswith('l'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 1.3 What is the difference between the following two lines of codes? Which one will give a larger value? Will this be the case for other texts?\n",
    "# sorted(set(w.lower() for w in text2))\n",
    "# sorted(w.lower() for w in set(text2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6403"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sorted(set(w.lower() for w in text2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6833"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sorted(w.lower() for w in set(text2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The difference between the two lines is the first line first lowers the cases of the tokens in text2 then filters out the unique ones, while the second line first filters out the unique tockens in text2 then lowers their cases. It turns out the second line gives a larger value since it could treat the same words but different cases as different words. This will also be the case for other texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 1.4 Find all the Twelve-letter words in the Chat Corpus (text2). With the help of a frequency distribution (FreqDist), show these words in decreasing order of frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdist5 = FreqDist(sorted(w for w in text5 if len(w) == 12 and w.isalpha()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('conversation', 5),\n",
       " ('construction', 3),\n",
       " ('entertaining', 3),\n",
       " ('multitasking', 2),\n",
       " ('Bloooooooood', 1),\n",
       " ('Christianity', 1),\n",
       " ('Connecticutt', 1),\n",
       " ('Considerably', 1),\n",
       " ('Constitution', 1),\n",
       " ('Fergalicious', 1),\n",
       " ('alternatives', 1),\n",
       " ('bachelorette', 1),\n",
       " ('butterscotch', 1),\n",
       " ('catastrophic', 1),\n",
       " ('christianity', 1),\n",
       " ('constituents', 1),\n",
       " ('denomination', 1),\n",
       " ('descriminate', 1),\n",
       " ('disappointed', 1),\n",
       " ('freeeezinggg', 1),\n",
       " ('hahahahahaha', 1),\n",
       " ('heartbreaker', 1),\n",
       " ('heyyyyyyyyyy', 1),\n",
       " ('hhaaaaatttee', 1),\n",
       " ('hugsssssssss', 1),\n",
       " ('ihavehotnips', 1),\n",
       " ('interruption', 1),\n",
       " ('masturbating', 1),\n",
       " ('necromancers', 1),\n",
       " ('neighborhood', 1),\n",
       " ('oooooooooooo', 1),\n",
       " ('outrageously', 1),\n",
       " ('passionately', 1),\n",
       " ('periodically', 1),\n",
       " ('psychologist', 1),\n",
       " ('registration', 1),\n",
       " ('slkfjsldkfjs', 1),\n",
       " ('subscription', 1),\n",
       " ('sweeeeeeeeet', 1),\n",
       " ('thanksgiving', 1),\n",
       " ('tooooooooooo', 1),\n",
       " ('yummylicious', 1)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist5.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 1.5 Review the discussion of looping with conditions in Section 4 in Chapter 1. Use a combination of for and if statements to loop over the tokens of text2 and print all the distinct punctuation, one per line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",'\n",
      ",-\n",
      "),\n",
      ";\"--\n",
      "?)\n",
      ":--\"\n",
      ">\n",
      "...\"\n",
      ";--\"\n",
      "?--\"\n",
      "?--\n",
      ",--\"\n",
      "!\"--\n",
      ".--\n",
      "--'\n",
      "'--\n",
      ",)\n",
      "?\n",
      ";--\n",
      "'\n",
      "!'\n",
      ";\n",
      "?\"--\n",
      "(\n",
      "!\n",
      ".)\n",
      ".]\n",
      ",\"\n",
      "\"\n",
      "--,\n",
      ",--\n",
      "-?\n",
      ".'\n",
      ".'\"\n",
      "\"--\n",
      "?'\n",
      "&\n",
      "!'\"\n",
      ".'--\n",
      ")\n",
      "***\n",
      ".\"\n",
      "',\n",
      ";'\n",
      ".--\"\n",
      ")--\n",
      "\"'\n",
      "!\"\n",
      ",\n",
      "--\"\n",
      "-?\"\n",
      ",\"--\n",
      ";\"\n",
      "[\n",
      "?\"\n",
      "--\n",
      ".\"--\n",
      "!--\"\n",
      ":\n",
      "-\n",
      "!--\n",
      ":--\n",
      "...\n",
      ".)--\n",
      "--(\n",
      ".\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "for token in set(text2):\n",
    "    if not token.isalnum():\n",
    "        print(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chapter 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 2.1 Use Gutenberg Corpus Module to explore 'shakespeare-caesar.txt'.a. How many word tokens does this book have? b.How many word types? c. What is the lexical richness?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['austen-emma.txt',\n",
       " 'austen-persuasion.txt',\n",
       " 'austen-sense.txt',\n",
       " 'bible-kjv.txt',\n",
       " 'blake-poems.txt',\n",
       " 'bryant-stories.txt',\n",
       " 'burgess-busterbrown.txt',\n",
       " 'carroll-alice.txt',\n",
       " 'chesterton-ball.txt',\n",
       " 'chesterton-brown.txt',\n",
       " 'chesterton-thursday.txt',\n",
       " 'edgeworth-parents.txt',\n",
       " 'melville-moby_dick.txt',\n",
       " 'milton-paradise.txt',\n",
       " 'shakespeare-caesar.txt',\n",
       " 'shakespeare-hamlet.txt',\n",
       " 'shakespeare-macbeth.txt',\n",
       " 'whitman-leaves.txt']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.gutenberg.fileids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "caesar = nltk.corpus.gutenberg.words('shakespeare-caesar.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25833"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a. Count of word tokens\n",
    "len(caesar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3560"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# b. Count of word types\n",
    "len(set(caesar))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1378082297836101"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lexical richness\n",
    "len(set(caesar)) / len(caesar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 2.2 Explore the Section \"Hobbies\" in Brown Corpus. Count the number of wh words, such as what, when, where, who and why. Please make sure we are not double-counting words like “This” and “this”, which differ only in capitalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import brown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['adventure',\n",
       " 'belles_lettres',\n",
       " 'editorial',\n",
       " 'fiction',\n",
       " 'government',\n",
       " 'hobbies',\n",
       " 'humor',\n",
       " 'learned',\n",
       " 'lore',\n",
       " 'mystery',\n",
       " 'news',\n",
       " 'religion',\n",
       " 'reviews',\n",
       " 'romance',\n",
       " 'science_fiction']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown.categories()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "hobbies_text = brown.words(categories = 'hobbies')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdist = nltk.FreqDist(w.lower() for w in hobbies_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "wh_words = ['what', 'when', 'where', 'who', 'why']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what: 108 when: 164 where: 77 who: 104 why: 17 "
     ]
    }
   ],
   "source": [
    "for wh in wh_words:\n",
    "    print(wh + ':', fdist[wh], end = ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 2.3 Explore movie reviews corpus which contains 2k movie reviews with sentiment polarity classification (positive or negative reviews). Please find out the 20 most common words in the negative reviews and positive reviews,please get rid of stop words, number and punctuations from reviews. Please make sure we are not double-counting words like “This” and “this”, which differ only in capitalization. \n",
    "# Hint:\n",
    "#from nltk.corpus import movie_reviews\n",
    "#from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import movie_reviews\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neg', 'pos']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_reviews.categories()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_movie_reviews_text = movie_reviews.words(categories = 'neg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = nltk.corpus.stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_content = [w for w in neg_movie_reviews_text if w.lower() not in stopwords and w.isalpha()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdist_neg = FreqDist(neg_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('film', 4287),\n",
       " ('movie', 3246),\n",
       " ('one', 2800),\n",
       " ('like', 1888),\n",
       " ('even', 1386),\n",
       " ('time', 1168),\n",
       " ('good', 1163),\n",
       " ('would', 1090),\n",
       " ('get', 1052),\n",
       " ('bad', 1034),\n",
       " ('much', 1011),\n",
       " ('character', 942),\n",
       " ('story', 923),\n",
       " ('plot', 917),\n",
       " ('two', 912),\n",
       " ('characters', 873),\n",
       " ('make', 851),\n",
       " ('first', 832),\n",
       " ('could', 791),\n",
       " ('see', 784)]"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist_neg.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('film', 5230),\n",
       " ('one', 3052),\n",
       " ('movie', 2525),\n",
       " ('like', 1802),\n",
       " ('good', 1248),\n",
       " ('story', 1246),\n",
       " ('time', 1243),\n",
       " ('also', 1200),\n",
       " ('even', 1179),\n",
       " ('well', 1123),\n",
       " ('character', 1078),\n",
       " ('life', 1057),\n",
       " ('much', 1038),\n",
       " ('would', 1019),\n",
       " ('first', 1004),\n",
       " ('two', 999),\n",
       " ('characters', 986),\n",
       " ('see', 965),\n",
       " ('way', 929),\n",
       " ('get', 897)]"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_movie_reviews_text = movie_reviews.words(categories = 'pos')\n",
    "pos_content = [w for w in pos_movie_reviews_text if w.lower() not in stopwords and w.isalpha()]\n",
    "fdist_pos = FreqDist(pos_content)\n",
    "fdist_pos.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 2.4 Explore Names Corpus, which initial letters are more frequent for males vs. females? ((Hint: Plot of a Conditional Frequency Distribution will be useful for answering this question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = nltk.corpus.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['female.txt', 'male.txt']"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.fileids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_names=names.words('male.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_names=names.words('female.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfd = nltk.ConditionalFreqDist(\n",
    "           (fileid, name[0])\n",
    "           for fileid in names.fileids()\n",
    "           for name in names.words(fileid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2209b557710>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 2.5 Use the Gutenberg Corpus module to explore austen-persuasion.txt. Write a function to find out the 20 most frequent occurring words of this text file that are not stopwords.Please get rid of numbers and punctuations. Please make sure we are not double-counting words like “This” and “this”, which differ only in capitalization.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "persuasion = nltk.corpus.gutenberg.words('austen-persuasion.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frequent_words(text):\n",
    "    stopwords = nltk.corpus.stopwords.words('english')\n",
    "    content = [w for w in text if w.lower() not in stopwords]\n",
    "    text_vocab = [w.lower() for w in content if w.isalpha()]\n",
    "    fdist = FreqDist(text_vocab)\n",
    "    return fdist.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('anne', 497),\n",
       " ('could', 451),\n",
       " ('would', 355),\n",
       " ('captain', 303),\n",
       " ('mrs', 291),\n",
       " ('elliot', 289),\n",
       " ('mr', 256),\n",
       " ('one', 238),\n",
       " ('must', 228),\n",
       " ('wentworth', 218),\n",
       " ('lady', 216),\n",
       " ('much', 205),\n",
       " ('good', 187),\n",
       " ('little', 176),\n",
       " ('said', 173),\n",
       " ('charles', 166),\n",
       " ('might', 166),\n",
       " ('well', 163),\n",
       " ('never', 155),\n",
       " ('time', 152)]"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_words(persuasion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question 2.6 Use one of the path similarity measures to score the similarity of each of the following pairs of words. Rank the pairs in order of decreasing similarity: car-automobile,  journey-voyage, boy-lad, coast-shore,  midday-noon, furnace-stove, food-fruit, bird-cock, bird-crane, tool-implement,   journey-car,  cemetery-woodland, food-rooster, coast-hill, forest-graveyard, shore-woodland, coast-forest, lad-wizard, chord-smile, glass-magician, noon-string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet as wn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = ['car','automobile','journey','voyage','boy','lad','coast','shore','midday','noon','furnace','stove','food','fruit','bird','cock','bird','crane','tool','implement','journey','car','cemetery','woodland','food','rooster','coast','hill','forest','graveyard','shore','woodland','coast','forest','lad','wizard','chord','smile','glass','magician','noon','string']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = {}\n",
    "for i in range(len(word_list)):\n",
    "    if (i % 2) == 0:\n",
    "        first = wn.synsets(word_list[i])[0]\n",
    "        second = wn.synsets(word_list[i + 1])[0]\n",
    "        similarity[word_list[i] + '-' + word_list[i + 1]] = first.path_similarity(second)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('car-automobile', 1.0),\n",
       " ('midday-noon', 1.0),\n",
       " ('coast-shore', 0.5),\n",
       " ('tool-implement', 0.5),\n",
       " ('boy-lad', 0.3333333333333333),\n",
       " ('journey-voyage', 0.25),\n",
       " ('coast-hill', 0.2),\n",
       " ('shore-woodland', 0.2),\n",
       " ('lad-wizard', 0.2),\n",
       " ('bird-crane', 0.1111111111111111),\n",
       " ('cemetery-woodland', 0.1111111111111111),\n",
       " ('glass-magician', 0.1111111111111111),\n",
       " ('food-fruit', 0.09090909090909091),\n",
       " ('coast-forest', 0.09090909090909091),\n",
       " ('chord-smile', 0.09090909090909091),\n",
       " ('furnace-stove', 0.07692307692307693),\n",
       " ('forest-graveyard', 0.07142857142857142),\n",
       " ('bird-cock', 0.0625),\n",
       " ('food-rooster', 0.0625),\n",
       " ('noon-string', 0.058823529411764705),\n",
       " ('journey-car', 0.05)]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import operator\n",
    "sorted(similarity.items(), key = operator.itemgetter(1), reverse = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}