resolved
Your Name
7 years ago
commit
d80696b948
@ -1,82 +0,0 @@
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import nltk
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from sys import stdin, stdout
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# Define input
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input = stdin.read()
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# FILTER FUNCTIONS
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# This function cuts a string into words. Then runs a POS tagger for each word. Returns a list with tags
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def postagger(string):
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words = nltk.word_tokenize(string)
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taggedwordlist = nltk.pos_tag(words)
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for word, pos in nltk.pos_tag(words):
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taggedwordlist = nltk.pos_tag(words)
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#print('{0} is a {1}'.format(word,pos)) # Comment out to print the analysis step
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taglist = [ pos for word,pos in taggedwordlist ]
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#print(taglist)
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return taglist;
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# This function changes the tags to readable equivalents (NNP to noun for example)
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def postagger_readable(list):
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readabletaglist = []
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for tag in list:
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if tag in {"NNP","NNS","NN","NNPS"}:
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readabletag = 'noun'
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elif tag in {'VB','VBD','VBG','VBN','VBP','VBZ'}:
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readabletag = 'verb'
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elif tag in {'RB','RBR','RBS','WRB'}:
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readabletag = 'adverb'
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elif tag in {'PRP','PRP$'}:
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readabletag = 'pronoun'
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elif tag in {'JJ','JJR','JJS'}:
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readabletag = 'adjective'
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elif tag == 'IN':
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readabletag = 'preposition'
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elif tag == 'WDT':
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readabletag = 'determiner'
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elif tag in {'WP','WP$'}:
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readabletag = 'pronoun'
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elif tag == 'UH':
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readabletag = 'interjection'
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elif tag == 'POS':
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readabletag = 'possesive ending'
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elif tag == 'SYM':
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readabletag = 'symbol'
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elif tag == 'EX':
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readabletag = 'existential there'
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elif tag == 'DT':
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readabletag = 'determiner'
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elif tag == 'MD':
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readabletag = 'modal'
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elif tag == 'LS':
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readabletag = 'list item marker'
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elif tag == 'FW':
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readabletag = 'foreign word'
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elif tag == 'CC':
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readabletag = 'coordinating conjunction '
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elif tag == 'CD':
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readabletag = 'cardinal number'
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elif tag == 'TO':
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readabletag = 'to'
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elif tag == '.':
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readabletag = 'line ending'
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elif tag == ',':
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readabletag = 'comma'
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else:
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readabletag = tag
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readabletaglist.append(readabletag)
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return readabletaglist;
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# This function creates the output
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def main():
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taglist = postagger(input)
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readabletaglist = postagger_readable(taglist)
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stdout.write(' '.join(readabletaglist))
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stdout.write('\n')
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main()
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File diff suppressed because one or more lines are too long
@ -0,0 +1,64 @@
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$(document).ready(function(){
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var state = 0;
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$('.noun').addClass('fade-out');
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$('.preposition').addClass('red');
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$('.verb').addClass('blue');
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$('.determiner').addClass('cyan');
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$(document).bind('contextmenu', function(e) { return false; });
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$( ".word" ).contextmenu(function() {
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console.log($(this).hasClass('underline'));
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$(this).hasClass('underline') == false
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? $(this).addClass('underline')
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: $(this).removeClass('underline');
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});
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$('.word').click( function() {
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var el = $('.word');
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console.log(state);
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if (state == 0) {
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$('.word').removeClass('fade-out red blue cyan');
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$('.stopword').addClass('fade-out');
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}
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else if (state == 1) {
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$('.stopword').removeClass('fade-out');
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$('.neutral').addClass('fade-out');
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}
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else if (state == 2) {
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$('.neutral').removeClass('fade-out');
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$('.noun').addClass('fade-out');
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$('.preposition').addClass('red');
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$('.verb').addClass('blue');
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state = -1;
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}
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$('.word').each(function() {
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var el = $(this);
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if (state == 0) {
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el.empty();
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el.html(el.data("stopword") + " ");
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}
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else if (state == 1) {
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el.empty();
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el.html(el.data("sentiment") + " ");
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}
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else {
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el.empty();
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el.html(el.data("pos") + " ");
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}
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});
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state = state+1;
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});
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});
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@ -0,0 +1,86 @@
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* {
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min-height: 0;
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min-width: 0;
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}
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body {
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background: #639ab2;
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font-size: 15px;
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font-family: 'Ubuntu Mono', monospace;
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}
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.prelative {
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flex-shrink: 0;
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}
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div.container {
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width: 100%;
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display: -webkit-box; /* OLD - iOS 6-, Safari 3.1-6 */
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display: -moz-box; /* OLD - Firefox 19- (buggy but mostly works) */
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display: -ms-flexbox; /* TWEENER - IE 10 */
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display: -webkit-flex; /* NEW - Chrome */
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display: flex;
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flex-wrap: wrap;
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}
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.word {
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font-size: 3rem;
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float: left;
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position: relative;
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text-align: center;
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display: -webkit-box; /* OLD - iOS 6-, Safari 3.1-6 */
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display: -moz-box; /* OLD - Firefox 19- (buggy but mostly works) */
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display: -ms-flexbox; /* TWEENER - IE 10 */
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display: -webkit-flex; /* NEW - Chrome */
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display:flex;
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justify-content: center;
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}
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.word:before,
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.word:after {
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content: '';
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color: #fff;
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position: absolute;
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font-family: 'PT Serif', serif;
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font-weight: bold;
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font-size: 1.5rem;
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font-style: italic;
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opacity: 0;
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width: 100%;
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}
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.word:before {
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content: attr(data-txt);
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flex-shrink: 1;
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}
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.word:hover:before,
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.word:active:after {
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opacity: 1;
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}
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.fade-out {
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color: #275152;
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}
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p {
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margin: 1rem;
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}
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.red {
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color: red;
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}
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.blue {
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color: blue;
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}
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.cyan {
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color: cyan;
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}
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.underline {
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text-decoration: underline;
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}
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@ -0,0 +1,20 @@
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<!DOCTYPE html>
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<html>
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<head>
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<title>Wordtagger</title>
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<meta charset="utf-8" />
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<link rel="stylesheet" href="style.css" type="text/css" media="screen" />
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<script type="text/javascript" src="jquery.min.js"></script>
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<script type="text/javascript" src="script.js"></script>
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<!--meta name="viewport" content="width=device-width"-->
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</head>
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<body>
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<div class="container"><p>
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{% for item, value in words_and_tags.items() %}
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<span id="{{item}}" class="word {{words_and_tags[item]['sentiment']}} {{words_and_tags[item]['wordtype']}} {{words_and_tags[item]['POS']}}" data-txt="{{ words_and_tags[item]['word'] }}" data-pos="{{words_and_tags[item]['POS']}}" {% if words_and_tags[item]['word'] in [',','.','(',')'] %} data-sentiment= "{{ words_and_tags[item]['word'] }}" {% else %} data-sentiment= '{{ words_and_tags[item]['sentiment'] }}' {% endif %} {% if words_and_tags[item]['wordtype'] == 'stopword' %} data-stopword= "stopword" {% else %} data-stopword= '{{ words_and_tags[item]['word'] }}' {% endif %} >{{words_and_tags[item]['POS']}} </span>
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{% endfor %}
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</p>
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</div>
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</body>
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</html>
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@ -0,0 +1,156 @@
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# LIBS
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import nltk
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import json
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import os
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from sys import stdin, stdout
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from nltk import ne_chunk, pos_tag, word_tokenize
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from nltk.sentiment.vader import SentimentIntensityAnalyzer
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from nltk.corpus import stopwords
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from jinja2 import Template
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# == INPUT AND TOKENIZE ==
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# Define input, tokenize and safe tokens to dictionary. Use index as ID for each word.
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input = stdin.read()
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words = nltk.word_tokenize(input)
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words_and_tags = {'item ' + str(index) : {'word':word} for index , word in enumerate(words)}
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print(words_and_tags)
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# == FILTER FUNCTIONS ==
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# === 1. POS_tagger & Named Entity Recognizer ===
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# This function cuts a string into words. Then runs a POS tagger for each word. Returns a list with tags
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def POS_tagger(list):
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taggedwordlist = nltk.pos_tag(list)
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for word, pos in nltk.pos_tag(list):
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taggedwordlist = nltk.pos_tag(list)
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#print('{0} is a {1}'.format(word,pos)) # Comment out to print the analysis step
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print(taggedwordlist)
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taglist = [ pos for word,pos in taggedwordlist ]
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POS_tags = []
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for tag in taglist:
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if tag in {"NNP","NNS","NN","NNPS"}:
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POS_tag = 'noun'
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elif tag in {'VB','VBD','VBG','VBN','VBP','VBZ'}:
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POS_tag = 'verb'
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elif tag in {'RB','RBR','RBS','WRB'}:
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POS_tag = 'adverb'
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elif tag in {'PRP','PRP$'}:
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POS_tag = 'pronoun'
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elif tag in {'JJ','JJR','JJS'}:
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POS_tag = 'adjective'
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elif tag == 'IN':
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POS_tag = 'preposition'
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elif tag == 'WDT':
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POS_tag = 'determiner'
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elif tag in {'WP','WP$'}:
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POS_tag = 'pronoun'
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elif tag == 'UH':
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POS_tag = 'interjection'
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elif tag == 'POS':
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POS_tag = 'possesive ending'
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elif tag == 'SYM':
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POS_tag = 'symbol'
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elif tag == 'EX':
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POS_tag = 'existential there'
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elif tag == 'DT':
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POS_tag = 'determiner'
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elif tag == 'MD':
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POS_tag = 'modal'
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elif tag == 'LS':
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POS_tag = 'list item marker'
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elif tag == 'FW':
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POS_tag = 'foreign word'
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elif tag == 'CC':
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POS_tag = 'coordinating conjunction '
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elif tag == 'CD':
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POS_tag = 'cardinal number'
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elif tag == 'TO':
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POS_tag = 'to'
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elif tag == '.':
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POS_tag = 'line ending'
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elif tag == ',':
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POS_tag = 'comma'
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else:
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POS_tag = tag
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POS_tags.append(POS_tag)
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#print(POS_tag)
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return POS_tags;
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# === 2. Sentiment tagger ===
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# Sentiment analyzer based on the NLTK VADER tagger.
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# This function uses words as an input. It tags each word based on its sentiment: negative, neutral or positive
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def sentiment_tagger(word):
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analyzer = SentimentIntensityAnalyzer()
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score = analyzer.polarity_scores(word).get("compound")
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if score < 0:
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sentiment_tag = 'negative'
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elif score > 0:
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sentiment_tag = 'positive'
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else:
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sentiment_tag = 'neutral'
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return sentiment_tag
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# === 3. Stopword tagger ===
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# Labels words on being a keyword or a stopword, based on the list in the NLTK corpus
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def stopword_tagger(word):
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stopWords = set(stopwords.words('english'))
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if word in stopWords:
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stopword_tag = 'stopword'
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else:
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stopword_tag = 'keyword'
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return stopword_tag
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# Run POS tagger
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# This tagger outputs a list for all items in the dict at once
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# To avoid double work, it is better to keep this outside the for loop
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POS_tags = POS_tagger(words)
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i = 0
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# Adding tags to words in dictionary, which will be exported as a json file
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# {'item 0' : {'word' : word, 'tagger 1': value 1}}
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for item, value in words_and_tags.items():
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word = words_and_tags[item]['word']
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# POS
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pos_tag = POS_tags[i]
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words_and_tags[item]['POS'] = pos_tag
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i = i+1
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# Add sentiment tag
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sentiment_tag = sentiment_tagger(word)
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words_and_tags[item]['sentiment'] = sentiment_tag
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# Add stopword tag
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stopword_tag = stopword_tagger(word)
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words_and_tags[item]['wordtype'] = stopword_tag
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# Add entity tag
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# Not functional yet
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# Save data into a json file
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print(words_and_tags)
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#with open("data.json", 'w') as f:
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with open(os.path.dirname(os.path.dirname(os.path.dirname( __file__ ))) + "output/wordtagger/data.json", 'w') as f:
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json.dump(words_and_tags, f, ensure_ascii=False)
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#let's bind it to a jinja2 template
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# Jinja moves up one level by default, so I do not need to do it myself as in line 141
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template_open = open("src/wordtagger/template.html", "r")
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template = Template(template_open.read())
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index_render = template.render(words_and_tags=words_and_tags)
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#print(text_render)
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# And render an html file!
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print(index_render)
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index_open = open("output/wordtagger/index.html", "w")
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index_open.write(index_render)
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index_open.close()
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Loading…
Reference in New Issue