JonathanReeve/Chiaroscuro Detector.ipynb

## Chiaroscuro Detector.ipynb
{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "#Appendix 1: Annotated Source Code for the Chiaroscuro Words Detector \n\nThis program counts the number of words in a text that pertain to lightness or darkness, as determined by the Princeton Wordnet hyponym tree for those concepts. See the appendices at the bottom of this page for a list of words used. "
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Display plots in this window.  \n%matplotlib inline\n\nimport nltk #using the Python Natural Language Toolkit\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus import wordnet\n\n#Logging\nimport logging\nlogger = logging.getLogger()\nlogger.setLevel(logging.WARN)\n\ndef readText(filename): \n    \"\"\" \n    read custom text file (text of novel from Project Gutenberg) \n    and return NLTK Text object\n    \"\"\"\n    logging.debug('Opening text file...')\n    textfile=open(filename,'rU').read() \n    #tokens = nltk.word_tokenize(file)\n    #text = nltk.Text(tokens)\n    logging.debug('Text file read successfully.')\n    \n    logging.debug('Tokenizing text.')\n    tokenizer = RegexpTokenizer(r'\\w+')\n    tokens = tokenizer.tokenize(textfile)\n    logging.debug('Tokenizing completed.')\n    return tokens\n\ndef cleanText(tokens): \n    \"\"\"\n    clean the text by removing puncuation, making all tokens lowercase, and lemmatizing. \n    \"\"\" \n    logging.debug('Cleaning text...')\n    # normalize words by making them all lowercase\n    tokensLower = [word.lower() for word in tokens] \n    from nltk.corpus import stopwords\n    englishStopwords = stopwords.words('english')\n    tokensNostops = [word for word in tokensLower if word not in englishStopwords]\n    return tokensNostops\n\ndef flattenList(biglist): \n    return [item for sublist in biglist for item in sublist]\n\ndef replaceUnderscores(wordList): \n    newList = [] \n    for word in wordList: \n        newWord = word.replace('_', ' ')\n        newList.append(newWord)\n    return newList\n\ndef getDarkAndLightWords(): \n    \"\"\" \n    Uses wordnet to generate words pertaining to lightness and darkness, \n    specifically, hyponyms of \"dark,\" \"darkness,\" \"light,\" and \"lightness.\" \n    \"\"\"\n    darks = wordnet.synsets('dark') + wordnet.synsets('darkness')\n    lights = wordnet.synsets('light') + wordnet.synsets('lightness')\n    darksSyns = [syn.hyponyms() for syn in darks]\n    lightsSyns = [syn.hyponyms() for syn in lights]\n    \n    # Flatten the lists, so that it returns a one-dimensional array. \n    flatDarks = flattenList(darksSyns)\n    flatLights = flattenList(lightsSyns)\n    \n    # Get all word forms for all of the words in our synsets. \n    darksLemmas = [[str(lemma.name()) for lemma in flatDark.lemmas()] for flatDark in flatDarks] \n    lightsLemmas = [[str(lemma.name()) for lemma in flatLight.lemmas()] for flatLight in flatLights]\n    darksList = flattenList(darksLemmas)\n    lightsList = flattenList(lightsLemmas)\n    \n    # Turn underscores into spaces. \n    finalDarkWords = replaceUnderscores(darksList)\n    finalLightWords = replaceUnderscores(lightsList)\n    \n    # Let's take all bigrams and break them up, since a search for \"star\" will match on \n    # \"shooting star,\" anyway, and \"beam of light\" will be matched by searching for the words \n    # \"beam\" and \"light.\" We'll filter out non-unique words, too, since breaking up \"beam of light\" \n    # will give us an extra \"beam\" and an extra \"light.\" \n    curatedDarkWords = list(set(flattenList([word.split(' ') for word in finalDarkWords])))\n    curatedLightWords = list(set(flattenList([word.split(' ') for word in finalLightWords])))\n    \n    wordsToRemove = ['fairy', 'fatuus', 'priming', 'room', 'pocket', 'buoyancy', 'euphoria', 'fuse', 'signal', 'sconce', 'friars', 'of', 'theater', 'ignuus']\n    wordsToAdd = ['brightness', 'bright', 'light', 'sun', 'sunshine', 'sunlight', 'sunlit', 'sunstruck', 'ablaze']\n    for word in wordsToRemove: \n        if word in curatedLightWords: \n            curatedLightWords.remove(word)\n    for word in wordsToAdd: \n        if word not in curatedLightWords: \n            curatedLightWords.append(word)\n\n    darkWordsToRemove = ['wedding', 'weeknight']\n    darkWordsToAdd = ['dim', 'fog', 'dark', 'shadow', 'shade', 'fog', 'dingy', 'dismal', 'gloomy', 'gloom', 'black']\n\n    for word in darkWordsToRemove: \n        if word in curatedDarkWords: \n            curatedDarkWords.remove(word)\n    for word in darkWordsToAdd: \n        if word not in curatedDarkWords: \n            curatedDarkWords.append(word)\n    \n    return curatedDarkWords, curatedLightWords",
     "prompt_number": 11,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "def countChiaroscuro(filename): \n    tokens = readText(filename)\n    logging.debug('Creating text object...')\n    text = nltk.Text(tokens)\n    newtokens = cleanText(tokens)\n    \n    # get lists of dark and light words \n    finalDarkWords, finalLightWords = getDarkAndLightWords()\n    \n    # initialize variables\n    wordCountBright=0\n    wordCountDark=0\n\n    #count words cumulatively\n    for word in finalLightWords: \n        wordCountBright=wordCountBright+text.count('word')\n    for word in finalDarkWords:\n        wordCountDark=wordCountDark+text.count('word')\n\n    totalWords=len(text)\n    print(\"\\nTotals for text \" + filename + \":\")\n    print(\"Total words in text: \"+str(totalWords))\n    print(\"Bright words: \"+str(wordCountBright))\n    print(\"Dark words: \"+str(wordCountDark))\n    wordCountBright=float(wordCountBright) #python 2 needs these to be floating point in order to divide them properly\n    wordCountDark=float(wordCountDark) \n    totalWords=float(totalWords) \n    proportionBright=(wordCountBright/totalWords)\n    proportionDark=(wordCountDark/totalWords)\n    combinedProportion=(proportionBright+proportionDark)*100\n    print(\"Proportion of bright words: \" + str(proportionBright)) \n    print(\"Proportion of dark words: \" + str(proportionDark))\n    print(\"Combined proportion, as percentage (x100): \" + str(combinedProportion))\n    return proportionBright, proportionDark\n    \ndef plotChiaroscuro(textsToAnalyze, textLabels, setLabel): \n    import numpy as np\n    import matplotlib.pyplot as plt\n    plt.figure(num=None, figsize=(10, 6), dpi=80, facecolor='w', edgecolor='k')\n\n    texts = [countChiaroscuro(text) for text in textsToAnalyze] \n    brights = [x[0] for x in texts]\n    darks = [x[1] for x in texts]\n    maxSum = max([sum(x) for x in texts])\n    \n    N = len(texts)  # number of texts\n\n    ind = np.arange(N)    # the x locations for the groups\n    width = 0.5       # the width of the bars: can also be len(x) sequence\n    opacity = 0.4\n\n    p1 = plt.bar(ind, brights, width, color='r', alpha=opacity)\n    p2 = plt.bar(ind, darks, width, color='b', alpha=opacity, bottom=brights)\n\n    plt.ylabel('Scores')\n    plt.title('Proportions of Light and Dark Words in ' + setLabel)\n    plt.xticks(ind+width/2., textLabels)\n    plt.yticks(np.arange(0,maxSum,maxSum/10))\n    plt.legend( (p1[0], p2[0]), ('Bright Words', 'Dark Words') )\n\n    plt.show()",
     "prompt_number": 12,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# Experiment 1: Selected Victorian Novels\nCan we quantify the mezzotinted *Middlemarch* or the dark *Bleak House* as compared with other \"dark\" novels? "
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "setLabel = \"Selected Victorian Novels\" \ntextsToAnalyze = ['bh-text.txt', 'mm-text.txt', 'ud-text.txt', 'ts-text.txt', 'pp-text.txt']\ntextLabels = ('Bleak House', 'Middlemarch', 'Mysteries of Udolpho', 'Turn of the Screw', 'Pride and Prejudice')\nplotChiaroscuro(textsToAnalyze, textLabels, setLabel)",
     "prompt_number": 14,
     "outputs": [
      {
       "output_type": "stream",
       "text": "\nTotals for text bh-text.txt:\nTotal words in text: 362309\nBright words: 12928\nDark words: 2688\nProportion of bright words: 0.035682249129886365\nProportion of dark words: 0.007419081502253601\nCombined proportion, as percentage (x100): 4.310133063213996\n\nTotals for text mm-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 326961\nBright words: 8888\nDark words: 1848\nProportion of bright words: 0.02718367022366582\nProportion of dark words: 0.005652050244524576\nCombined proportion, as percentage (x100): 3.2835720468190397\n\nTotals for text ud-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 297288\nBright words: 3131\nDark words: 651\nProportion of bright words: 0.010531874814994215\nProportion of dark words: 0.0021897957536126584\nCombined proportion, as percentage (x100): 1.2721670568606873\n\nTotals for text ts-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 46624\nBright words: 1616\nDark words: 336\nProportion of bright words: 0.034660260809883325\nProportion of dark words: 0.007206588881262869\nCombined proportion, as percentage (x100): 4.186684969114619\n\nTotals for text pp-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 126070\nBright words: 4949\nDark words: 1029\nProportion of bright words: 0.039255968906163244\nProportion of dark words: 0.008162132148806218\nCombined proportion, as percentage (x100): 4.741810105496946\n",
       "stream": "stdout"
      },
      {
       "text": "<matplotlib.figure.Figure at 0x7f58a74d9128>",
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XX0mJWEPPMby3zN/fH7Nnz4afnx+cnJzqfVaxsfZ3hYeHIzQ0FEZGRvj2228b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\nERERyTBAJCIiIiIZBohEREREJPP//gdl0yKKAMoAAAAASUVORK5CYII=\n",
       "output_type": "display_data",
       "metadata": {}
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "#Experiment 2: Dickens Novels\nCan we quantify the \"darkness\" of Dickens's so-called \"dark novels\" (*Bleak House*, *Hard Times*, and *Little Dorrit*)? "
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "setLabel = \"Dickens Novels\" \ntextsToAnalyze = ['pickwick.txt', 'ot-text.txt', 'nn-text.txt', 'bh-text.txt', 'ht-text.txt', 'dorrit.txt', 'cities.txt']\ntextLabels = ('Pickwick', 'Oliver', 'Nickelby', 'Bleak', 'Hard', 'Little Dorrit', 'Tale of Two Cities')\nplotChiaroscuro(textsToAnalyze, textLabels, setLabel)",
     "prompt_number": 13,
     "outputs": [
      {
       "output_type": "stream",
       "text": "\nTotals for text pickwick.txt:\nTotal words in text: 313359\nBright words: 11110\nDark words: 2310\nProportion of bright words: 0.035454542553429134\nProportion of dark words: 0.007371736570514969\nCombined proportion, as percentage (x100): 4.28262791239441\n\nTotals for text ot-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 162312\nBright words: 8686\nDark words: 1806\nProportion of bright words: 0.05351421952782296\nProportion of dark words: 0.011126718911725566\nCombined proportion, as percentage (x100): 6.464093843954853\n\nTotals for text nn-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 331041\nBright words: 20301\nDark words: 4221\nProportion of bright words: 0.06132473016937479\nProportion of dark words: 0.012750686470860105\nCombined proportion, as percentage (x100): 7.40754166402349\n\nTotals for text bh-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 362309\nBright words: 12928\nDark words: 2688\nProportion of bright words: 0.035682249129886365\nProportion of dark words: 0.007419081502253601\nCombined proportion, as percentage (x100): 4.310133063213996\n\nTotals for text ht-text.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 109113\nBright words: 6767\nDark words: 1407\nProportion of bright words: 0.06201827463272021\nProportion of dark words: 0.012894888785021033\nCombined proportion, as percentage (x100): 7.491316341774123\n\nTotals for text dorrit.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 345378\nBright words: 16867\nDark words: 3507\nProportion of bright words: 0.048836347422244616\nProportion of dark words: 0.010154092038288483\nCombined proportion, as percentage (x100): 5.89904394605331\n\nTotals for text cities.txt:",
       "stream": "stdout"
      },
      {
       "output_type": "stream",
       "text": "\nTotal words in text: 138394\nBright words: 6767\nDark words: 1407\nProportion of bright words: 0.04889662846655202\nProportion of dark words: 0.01016662572076824\nCombined proportion, as percentage (x100): 5.906325418732026\n",
       "stream": "stdout"
      },
      {
       "text": "<matplotlib.figure.Figure at 0x7f58cc1ec438>",
       "png": 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1z06dOpGUlGROb926lcmTJ9vM27JlC506dQJg1apVhIeH4+XlRZcuXdi/f7/Z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\ngOuvv56cnJwK5a+66irat29PcnIy4eHhrF27ll69elGrVi3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       "output_type": "display_data",
       "metadata": {}
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# Appendix 2: Words Counted in this Analysis\n\n## Dark Synsets"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "for sense in darks:\n    print(sense.name() + ': ' + sense.definition())   ",
     "prompt_number": 35,
     "outputs": [
      {
       "output_type": "stream",
       "text": "dark.n.01: absence of light or illumination\niniquity.n.01: absence of moral or spiritual values\ndarkness.n.02: an unilluminated area\nnight.n.01: the time after sunset and before sunrise while it is dark outside\ndark.n.05: an unenlightened state\ndark.a.01: devoid of or deficient in light or brightness; shadowed or black\ndark.a.02: (used of color) having a dark hue\ndark.s.03: brunet (used of hair or skin or eyes)\nblack.s.05: stemming from evil characteristics or forces; wicked or dishonorable\ndark.s.05: secret\ndark.s.06: showing a brooding ill humor\nbenighted.s.02: lacking enlightenment or knowledge or culture\ndark.s.08: marked by difficulty of style or expression\nblue.s.08: causing dejection\ncolored.s.02: having skin rich in melanin pigments\ndark.s.11: not giving performances; closed\ndark.n.01: absence of light or illumination\ndarkness.n.02: an unilluminated area\niniquity.n.01: absence of moral or spiritual values\ndark.n.05: an unenlightened state\ndarkness.n.05: having a dark or somber color\ndarkness.n.06: a swarthy complexion\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "##Light Synsets"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "for sense in lights: \n    print(sense.name() + ': ' + sense.definition())   ",
     "prompt_number": 37,
     "outputs": [
      {
       "output_type": "stream",
       "text": "light.n.01: (physics) electromagnetic radiation that can produce a visual sensation\nlight.n.02: any device serving as a source of illumination\nlight.n.03: a particular perspective or aspect of a situation\nluminosity.n.01: the quality of being luminous; emitting or reflecting light\nlight.n.05: an illuminated area\nlight.n.06: a condition of spiritual awareness; divine illumination\nlight.n.07: the visual effect of illumination on objects or scenes as created in pictures\nlight.n.08: a person regarded very fondly\nlight.n.09: having abundant light or illumination\nlight.n.10: mental understanding as an enlightening experience\nsparkle.n.01: merriment expressed by a brightness or gleam or animation of countenance\nlight.n.12: public awareness\ninner_light.n.01: a divine presence believed by Quakers to enlighten and guide the soul\nlight.n.14: a visual warning signal\nlighter.n.02: a device for lighting or igniting fuel or charges or fires\nlight.v.01: make lighter or brighter\nlight_up.v.05: begin to smoke\nalight.v.01: to come to rest, settle\nignite.v.01: cause to start burning; subject to fire or great heat\nfall.v.20: fall to somebody by assignment or lot\nunhorse.v.01: alight from (a horse)\nlight.a.01: of comparatively little physical weight or density\nlight.a.02: (used of color) having a relatively small amount of coloring agent\nlight.a.03: of the military or industry; using (or being) relatively small or light arms or equipment\nlight.a.04: not great in degree or quantity or number\nlight.a.05: psychologically light; especially free from sadness or troubles\nlight.a.06: characterized by or emitting light\nunaccented.s.02: (used of vowels or syllables) pronounced with little or no stress\nlight.s.08: easily assimilated in the alimentary canal; not rich or heavily seasoned\nlight.s.09: (used of soil) loose and large-grained in consistency\nclean.s.03: (of sound or color) free from anything that dulls or dims\nlight.s.11: moving easily and quickly; nimble\nlight.s.12: demanding little effort; not burdensome\nlight.a.13: of little intensity or power or force\nlight.a.14: (physics, chemistry) not having atomic weight greater than average\nfaint.s.04: weak and likely to lose consciousness\nlight.s.16: very thin and insubstantial\nabstemious.s.02: marked by temperance in indulgence\nlight.s.18: less than the correct or legal or full amount often deliberately so\nlight.s.19: having little importance\nlight.s.20: intended primarily as entertainment; not serious or profound\nidle.s.04: silly or trivial\nlight.s.22: designed for ease of movement or to carry little weight\nlight.s.23: having relatively few calories\nlight.s.24: (of sleep) easily disturbed\neasy.s.10: casual and unrestrained in sexual behavior\nlightly.r.02: with few burdens\nelation.n.02: a feeling of joy and pride\nlightness.n.02: the property of being comparatively small in weight\nagility.n.01: the gracefulness of a person or animal that is quick and nimble\nlightness.n.04: having a light color\nlight.n.07: the visual effect of illumination on objects or scenes as created in pictures\nlightsomeness.n.03: the trait of being lighthearted and frivolous\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# Final Words Counted"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "darkWords, lightWords = getDarkAndLightWords()",
     "prompt_number": 20,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "print(lightWords)",
     "prompt_number": 23,
     "outputs": [
      {
       "output_type": "stream",
       "text": "['meteor', 'houselights', 'night-light', 'lantern', 'euphory', 'friction', 'searchlight', 'enkindle', 'candlelight', \"friar's\", 'irradiation', 'ray', 'fire', \"jack-o'-lantern\", 'inflame', 'sun', 'daylight', 'lighter', 'gegenschein', 'moonlight', 'half-light', 'anchor', 'sidelight', 'halo', 'fuzee', 'panel', 'shooting', 'sunshine', 'beam', 'conflagrate', 'brightness', 'running', 'floodlight', 'highlighting', 'glory', 'aureole', 'match', 'highlight', 'Moon', 'glow', 'kindle', 'illumination', 'glowing', 'riding', 'photoflood', 'twilight', 'sunniness', 'traffic', 'lamplight', \"will-o'-the-wisp\", 'flame', 'starlight', 'fluorescence', 'gloriole', 'flood', 'ignis', 'cigar', 'spotlight', 'fusee', 'counterglow', 'firelight', 'fuze', 'jacklight', 'torchlight', 'gaslight', 'light', 'blinker', 'nimbus', 'corona', 'navigation', 'headlight', 'radiance', 'lamp', 'shaft', 'torch', 'aura', 'cigarette', 'strip', 'lighting', 'airiness', 'lucifer', 'headlamp', 'candle', 'stoplight', 'star', 'flasher', 'up', 'sunlight', 'illuminance', 'incandescence', 'luminescence', 'streamer', 'primer', 'moonshine', 'reignite', 'flare', 'scintillation', 'bright', 'sunlit', 'sunstruck', 'ablaze']\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "print(darkWords)",
     "prompt_number": 25,
     "outputs": [
      {
       "output_type": "stream",
       "text": "['dimout', 'lightlessness', 'darkness', 'pitch', 'black', 'foulness', 'night', 'blackout', 'semidarkness', 'total', 'brownout', 'blackness', 'dim', 'fog', 'dark', 'shadow', 'shade', 'dingy', 'dismal', 'gloomy', 'gloom']\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    }
   ],
   "metadata": {}
  }
 ],
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