Natural Language Processing Personal Notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download necessary packages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 - Tokenizing\n",
    "Form of grouping\n",
    "    Word and sentence tokenizers\n",
    "    Corpora - body of text, ex: medical journals, presidential speeches, English language\n",
    "    Lexicon - words and their meainings\n",
    "        ex: investor speak vs regular speak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import sent_tokenize, word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_text = \"Hello there, how are you doing today? The weather is great and Python is awesome. The sky is pink\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seperate sentences using sent tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hello there, how are you doing today?', 'The weather is great and Python is awesome.', 'The sky is pink']\n"
     ]
    }
   ],
   "source": [
    "print(sent_tokenize(example_text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seperate words using word tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hello', 'there', ',', 'how', 'are', 'you', 'doing', 'today', '?', 'The', 'weather', 'is', 'great', 'and', 'Python', 'is', 'awesome', '.', 'The', 'sky', 'is', 'pink']\n"
     ]
    }
   ],
   "source": [
    "print(word_tokenize(example_text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "there\n",
      ",\n",
      "how\n",
      "are\n",
      "you\n",
      "doing\n",
      "today\n",
      "?\n",
      "The\n",
      "weather\n",
      "is\n",
      "great\n",
      "and\n",
      "Python\n",
      "is\n",
      "awesome\n",
      ".\n",
      "The\n",
      "sky\n",
      "is\n",
      "pink\n"
     ]
    }
   ],
   "source": [
    "for i in word_tokenize(example_text):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2 - Stop Words\n",
    "What is stop words? Words that you want to filter out of an analysis. Filler words basically, rendering them useless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_sentence = \"This is an example showing off stop word filtration.\"\n",
    "#set of stop words already predfined by nltk\n",
    "stop_words = set(stopwords.words(\"english\")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['This', 'example', 'showing', 'stop', 'word', 'filtration', '.']\n"
     ]
    }
   ],
   "source": [
    "words = word_tokenize(example_sentence)\n",
    "filtered_sentence = []\n",
    "for w in words:\n",
    "    if w not in stop_words:\n",
    "        filtered_sentence.append(w)\n",
    "print(filtered_sentence)        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the previous sentence that some words have been removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This', 'example', 'showing', 'stop', 'word', 'filtration', '.']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#short hand version of previous code\n",
    "filtered_sentence = [w for w in words if not w in stop_words]\n",
    "print(filtered_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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