Satz_IS602_corpus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "1. Choose a corpus of interest.\n",
    "    Dracula \n",
    "2. How many total unique words are in the corpus? (Please feel free to define unique words in any interesting, defensible way).\n",
    "    20,016 words after removing punctuation\n",
    "    \n",
    "3. Taking the most common words, how many unique words represent half of the total words in the corpus?\n",
    "    The top 88 words account for 50% of the text\n",
    "4. Identify the 200 highest frequency words in this corpus.\n",
    "    done, see script\n",
    "5. Create a graph that shows the relative frequency of these 200 words.\n",
    "    see below graph (the first)\n",
    "6. Does the observed relative frequency of these words follow Zipf’s law? Explain.\n",
    "    see below graph (the last one).  Yes, Zipf's law is followed, although the highest freq words diverge\n",
    "7. In what ways do you think the frequency of the words in this corpus differ from “all words in all corpora.”\n",
    "    Looking at the Zipfs law on wikipedia, shows a better fit for large amounts of material.  One theory is that\n",
    "    Zipfs law is followed due to laziness, perhaps novels attempt to avoid repeating words too often."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#open my file\n",
    "f = open('/Users/alexandersatz/Documents/Cuny/IS620/dracula.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# need to get rid of newline characters and convert to a string.\n",
    "# most annoyingly, theres some character that the tokenizer can't recognize.\n",
    "# and I need to ignore this\n",
    "s = ''\n",
    "for line in f:\n",
    "    line = line.strip()\n",
    "    line = line.decode('utf-8', 'ignore')\n",
    "    s = s + line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# now I can tokenize the string\n",
    "tokens = nltk.word_tokenize(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181846"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = nltk.FreqDist(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u',', 11394),\n",
       " (u'.', 6585),\n",
       " (u'the', 6410),\n",
       " (u'and', 5209),\n",
       " (u'I', 4356),\n",
       " (u'to', 3990),\n",
       " (u'of', 3273),\n",
       " (u'a', 2586),\n",
       " (u'in', 2216),\n",
       " (u'that', 2144)]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Many of these 'words' are punctuation, so I want to get rid of these\n",
    "f.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#open my file\n",
    "import string\n",
    "f = open('/Users/alexandersatz/Documents/Cuny/IS620/dracula.txt')\n",
    "s = ''\n",
    "exclude = set(string.punctuation)\n",
    "for line in f:\n",
    "    line = line.strip()\n",
    "    line = line.decode('utf-8', 'ignore')\n",
    "    line = ''.join(ch for ch in line if ch not in exclude)  ## get rid of punctuation\n",
    "    s = s + line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'the', 6362),\n",
       " (u'and', 4964),\n",
       " (u'I', 4058),\n",
       " (u'to', 3971),\n",
       " (u'of', 3259),\n",
       " (u'a', 2571),\n",
       " (u'in', 2196),\n",
       " (u'that', 2097),\n",
       " (u'he', 1724),\n",
       " (u'was', 1618)]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## now I am only looking at words\n",
    "tokens2 = nltk.word_tokenize(s)\n",
    "f2 = nltk.FreqDist(tokens2)\n",
    "f2.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20617"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = s.split()\n",
    "len(set(l1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fall = f2.most_common(20000)\n",
    "t = 0\n",
    "x = 0\n",
    "while t <0.5:\n",
    "    t += f2.freq(fall[x][0])\n",
    "    x += 1\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0422275321916899"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f2.freq('the')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "th = f2.most_common(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'a'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th[5][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lw = []\n",
    "lf = []\n",
    "for x in th:\n",
    "    lw.append(x[0])\n",
    "    lf.append(f2.freq(x[0]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alexandersatz/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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0s52BnVNwMBr4MzDL3R9KAcEL7n5GD8vfrIBAREREREQ66uubivcBVrj7and/DbgcmFVI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\n8/aiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160e5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(range(len(lw)-50))\n",
    "fig = plt.figure(figsize=(13, 5), dpi=100)\n",
    "plt.plot(x, lf[:-50], 'ro')\n",
    "plt.xticks(x, lw[:-50], rotation='vertical')\n",
    "#plt.margins(0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UzOxIM3vSzJ5Jj7E66jEdiJmVmdmfzKwu6rF0xsxeNbPn0j/Tp6IeT2fMbLCZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111cf5690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "xa = list(range(len(lw)))\n",
    "xb = []\n",
    "for x in xa:\n",
    "    xb.append(math.log(float(x+1),2))\n",
    "lnew = []\n",
    "for x in lf:\n",
    "    lnew.append(math.log(x,2))\n",
    "plt.plot(xb, lnew, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}