Messing With NLTK Text Methods.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#NLTK's Text objects are pretty neat and have a bunch of built-in methods.  Made a little demo of the sorts of things you can do\n",
      "import pandas as pd\n",
      "import nltk\n",
      "from nltk.util import ngrams\n",
      "nltk.download('punkt')\n",
      "nltk.download('stopwords')\n",
      "\n",
      "#Importing the dataset\n",
      "%cd C:\\Users\\Matt\\Dropbox\\Python Workspace\\CROW\\CROL-PDF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[nltk_data] Downloading package punkt to\n",
        "[nltk_data]     C:\\Users\\Matt\\AppData\\Roaming\\nltk_data...\n",
        "[nltk_data]   Package punkt is already up-to-date!\n",
        "[nltk_data] Downloading package stopwords to\n",
        "[nltk_data]     C:\\Users\\Matt\\AppData\\Roaming\\nltk_data...\n",
        "[nltk_data]   Package stopwords is already up-to-date!\n",
        "C:\\Users\\Matt\\Dropbox\\Python Workspace\\CROW\\CROL-PDF\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "file = open('rawCorpus.txt')\n",
      "t = file.read()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myCorpusTokenized = nltk.word_tokenize(t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corpusText = nltk.Text(myCorpusTokenized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#You can see the sorts of contexts a word tends to pop up in\n",
      "corpusText.concordance(\"Department\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Displaying 25 of 285 matches:\n",
        "                                  DEPARTMENT . The following listed property i\n",
        " call Verizon relay service . The Department of Citywide Administrative Servic\n",
        "commission or call 212-788-3071 . Department of Education Meets in the Hall of\n",
        "commission or call 212-788-3071 . Department of Education Meets in the Hall of\n",
        "commission or call 212-788-3071 . Department of Education Meets in the Hall of\n",
        "commission or call 212-788-3071 . Department of Education Meets in the Hall of\n",
        "commission or call 212-788-3071 . Department of Education Meets in the Hall of\n",
        "commission or call 212-788-3071 . Department of Education Meets in the Hall of\n",
        "commission or call 212-788-3071 . Department of Education Meets in the Hall of\n",
        "les of the City of New York , the Department of Transportation ( `` DOT '' ) i\n",
        "les of the City of New York , the Department of Transportation ( `` DOT '' ) i\n",
        "T TO LAW , that the New York City Department of Consumer Affairs will hold a P\n",
        " Fiscal Year 2016 . New York City Department of Transportation Notice of Publi\n",
        "blic hearing by the New York City Department of Transportation . The hearing w\n",
        "T TO LAW , that the New York City Department of Consumer Affairs will hold a P\n",
        "City Charter 312 ( a ) : Agency : Department of Information Technology & Telec\n",
        "F an application submitted by the Department of Housing Preservation and Devel\n",
        " PROGRAM CD funds are used by the Department of Housing Preservation and Devel\n",
        " the support of the New York City Department of Parks and Recreation , helps t\n",
        " VIOLATION REMOVAL IN SCHOOLS The Department of Education will use CD funds to\n",
        "square feet of patio area for the Department for the Aging to use as a Senior \n",
        "er use as the Commissioner of the Department of Citywide Administrative Servic\n",
        "c Hearing will be held at the NYC Department of Environmental Protection Offic\n",
        "proposed contract between the NYC Department of Environmental Protection and A\n",
        "F a proposed contract between the Department of Environmental Protection and C\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#You can see which words pop up in similar contexts to a given word\n",
      "corpusText.similar(\"Department\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "office city notice term board borough second construction point\n",
        "service operation renewal application north comptroller bonds state\n",
        "cellar rehabilitation administration\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#You can see what contexts (if any) a list of words share\n",
      "corpusText.common_contexts([\"Department\", \"Office\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "nan_of agency_of city_of nyc_of development_of york_of the_of\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#You can see graphs of what part of a text your word is in\n",
      "corpusText.dispersion_plot([\"City\",\"Department\",\"Office\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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       "output_type": "display_data",
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kSZIkSZIkSZIkSZIkSZIkScrXh4mv2kq+RnzvYHI28JadrLtL9U/XTccQ/+i5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\nkiRJkiRJkiRJkiRJkjT5/H+WacEXgfdsUgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5bc8ac8>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}