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179 lines
3.5 KiB
Python
179 lines
3.5 KiB
Python
from __future__ import division
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import glob
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from nltk import *
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import re
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import nltk
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import codecs
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from nltk import sent_tokenize, word_tokenize, pos_tag
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from nltk.probability import FreqDist
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from nltk.corpus import stopwords
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nltk.download('stopwords')
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#open the txt file, read, and tokenize
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file = open('faceapp.txt','r')
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text = file.read()
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#stopwords
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default_stopwords = set(stopwords.words('english'))
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custom_stopwords = set(codecs.open('stopwords.txt', 'r').read().splitlines())
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all_stopwords = default_stopwords | custom_stopwords
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print('''<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<title></title>
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<style>
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@font-face {
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font-family: "Belgika";
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src: url("http://bohyewoo.com/webfonts/belgika/belgika-40th-webfont.eot");
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src: url("http://bohyewoo.com/webfonts/belgika/belgika-40th-webfont.woff") format("woff"),
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url("http://bohyewoo.com/webfonts/belgika/belgika-40th-webfont.svg#filename") format("svg");
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}
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@font-face {
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font-family: "Belgika";
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src: url("http://bohyewoo.com/webfonts/belgika/belgika-16th-webfont.eot");
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src: url("http://bohyewoo.com/webfonts/belgika/belgika-16th-webfont.woff") format("woff"),
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url("http://bohyewoo.com/webfonts/belgika/belgika-16th-webfont.svg#filename") format("svg");
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}
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@font-face {
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font-family: "Belgika";
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src: url("http://bohyewoo.com/webfonts/belgika/belgika-8th-webfont.eot");
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src: url("http://bohyewoo.com/webfonts/belgika/belgika-8th-webfont.woff") format("woff"),
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url("http://bohyewoo.com/webfonts/belgika/belgika-8th-webfont.svg#filename") format("svg");
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}
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body {
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font-family: helvetica;
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font-weight: regular;
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letter-spacing: 0.5px;
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font-size: 20px;
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line-height: 1.2;
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}
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.NNP {
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background-color: pink;
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}
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.VBP {
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background-color: gold;
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}
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.NN {
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background-color: LightSkyBlue;
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}
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.NNS {
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background-color: Aquamarine;
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}
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.paragraph {
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width: 70%;
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float: right;
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}
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.top_words {
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font-size: 9pt;
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width: 25%;
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float: left;
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}
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</style>
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</head>
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<body>''')
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# my stopwords are common words I don't want to count, like "a", "an", "the".
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print('<div class ="paragraph">')
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for sentence in sent_tokenize(text):
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print('<span>')
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tokenized = word_tokenize(sentence)
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tagged = pos_tag(tokenized)
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# for HTML
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for word, pos in tagged:
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print('<span class="{}">{}</span>'.format(pos, word))
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print('</span>')
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print('</div>')
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# filtering stopwords
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tokens_without_stopwords = nltk.FreqDist(words.lower() for words in tokenized if words.lower() not in all_stopwords)
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print(tokens_without_stopwords)
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# for read_whole_text in tokens_without_stopwords:
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# whole_text_tokenized =
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# print(whole_text_tokenized)
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# #filtered words in sentence
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# filtered_sentence = (" ").join(tokens_without_stopwords)
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# print(filtered_sentence)
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print('<div class="top_words"> colonial words:')
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frequency_word = FreqDist(tokens_without_stopwords)
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top_words = tokens_without_stopwords.most_common(10)
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for chosen_words, frequency in top_words:
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print('<br><span class="chosen_words">{}({}) </span>'.format(chosen_words, frequency))
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print('''</div></body></html>''')
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# for new_file in tokens_without_stopwords:
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# appendFile = open('tokenized_words.txt', 'a')
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# appendFile.write(" " + new_file)
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# appendFile.close()
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# #shows only stopwords
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# processed_word_list = []
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# for word in tokenized:
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# # print(word)
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# if word not in all_stopwords:
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# processed_word_list.append('*')
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# else:
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# processed_word_list.append(word)
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# print(processed_word_list)
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# # # result putting in a graph
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# top_words_plot = frequency_word.plot(10)
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# print(top_words_plot)
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