worked on NLTK tokenize + POS tagger
parent
45ec6e0060
commit
cba64a7b99
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import nltk
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file=open('faceapp.txt','r')
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raw=file.read()
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tokens = nltk.word_tokenize(raw)
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faceapp = nltk.Text(tokens)
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# my stopwords are common words I don't want to count, like "a", "an", "the".
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stopwords = set(line.strip() for line in open('stopwords.txt'))
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# dictionary
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wordcount = {}
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# spliting words from punctuation so "book" and "book!" counts as the same word
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for word in raw.lower().split():
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word = word.replace(".","")
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word = word.replace(",","")
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word = word.replace(":","")
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word = word.replace("\"","")
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word = word.replace("!","")
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word = word.replace("“","")
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word = word.replace("‘","")
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word = word.replace("*","")
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word = word.replace("(","")
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word = word.replace(")","")
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faceapp.concordance('a')
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If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account . If you permit others to use your account credentials , you are responsible for the activities of such users that occur in connection with your account .
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* {
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margin:0;
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padding:0;
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border:0;
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outline:0;
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}
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body {
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font-family: Helvetica,sans-serif;
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font-weight: normal;
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letter-spacing: 1px;
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word-spacing: 2px;
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font-size: 16px;
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line-height: 1.3;
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}
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.menu {
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background: #c0c0c0;
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width: 100%;
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padding: 10px;
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font-size: 14px;
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position:fixed;
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}
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.faceapp {
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padding-top: 50px;
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padding-left: 10px;
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width: 40%;
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}
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.coloniality-100 {
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text-decoration: none;
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border-bottom: 5px solid red;
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}
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span:hover {
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color: grey;
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}
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.coloniality-90 {
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text-decoration: none;
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border-bottom: 4px solid blue;
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}
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.coloniality-50 {
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text-decoration: none;
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border-bottom: 3px solid #b0ff00;
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}
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.coloniality-40 {
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text-decoration: none;
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border-bottom: 2px solid pink;
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}
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.sentence:hover {
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background-color: #FFFF00
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}
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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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x = 1
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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: Belgika;
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# font-weight: 8th;
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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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}
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.VBP:hover {
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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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font-family: helvetica;
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font-weight: regular;
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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-family: Belgika;
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font-weight: 8th;
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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(text)
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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(100)
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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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# new_html = open('output.html', 'wb') # open the output file
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# new_html.write('''</div></body></html>''')
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# new_html.close() # close the output file
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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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@ -1,65 +0,0 @@
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* {
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margin:0;
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padding:0;
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border:0;
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outline:0;
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}
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body {
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font-family: Helvetica,sans-serif;
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font-weight: normal;
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letter-spacing: 1px;
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word-spacing: 2px;
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font-size: 16px;
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line-height: 1.3;
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}
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.menu {
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background: #c0c0c0;
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width: 100%;
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padding: 10px;
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font-size: 14px;
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position:fixed;
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}
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.faceapp {
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padding-top: 50px;
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padding-left: 10px;
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width: 40%;
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}
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.coloniality-100 {
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text-decoration: none;
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border-bottom: 5px solid red;
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}
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span:hover {
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color: grey;
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}
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.coloniality-90 {
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text-decoration: none;
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border-bottom: 4px solid blue;
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}
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.coloniality-50 {
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text-decoration: none;
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border-bottom: 3px solid #b0ff00;
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}
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.coloniality-40 {
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text-decoration: none;
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border-bottom: 2px solid pink;
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}
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.sentence:hover {
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background-color: #FFFF00
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}
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,198 @@
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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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x = 1
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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: Belgika;
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# font-weight: 8th;
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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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}
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.VBP:hover {
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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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font-family: helvetica;
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font-weight: regular;
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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-family: Belgika;
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font-weight: 8th;
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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(text)
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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(100)
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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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# new_html = open('output.html', 'wb') # open the output file
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# new_html.write('''</div></body></html>''')
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# new_html.close() # close the output file
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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')
|
||||
# appendFile.write(" " + new_file)
|
||||
# appendFile.close()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# #shows only stopwords
|
||||
# processed_word_list = []
|
||||
|
||||
# for word in tokenized:
|
||||
# # print(word)
|
||||
# if word not in all_stopwords:
|
||||
# processed_word_list.append('*')
|
||||
# else:
|
||||
# processed_word_list.append(word)
|
||||
# print(processed_word_list)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# # # result putting in a graph
|
||||
# top_words_plot = frequency_word.plot(10)
|
||||
# print(top_words_plot)
|
||||
@ -0,0 +1,178 @@
|
||||
from __future__ import division
|
||||
import glob
|
||||
from nltk import *
|
||||
import re
|
||||
|
||||
|
||||
import nltk
|
||||
import codecs
|
||||
from nltk import sent_tokenize, word_tokenize, pos_tag
|
||||
from nltk.probability import FreqDist
|
||||
from nltk.corpus import stopwords
|
||||
nltk.download('stopwords')
|
||||
|
||||
|
||||
#open the txt file, read, and tokenize
|
||||
file = open('faceapp.txt','r')
|
||||
text = file.read()
|
||||
|
||||
#stopwords
|
||||
default_stopwords = set(stopwords.words('english'))
|
||||
custom_stopwords = set(codecs.open('stopwords.txt', 'r').read().splitlines())
|
||||
all_stopwords = default_stopwords | custom_stopwords
|
||||
|
||||
print('''<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<title></title>
|
||||
<style>
|
||||
|
||||
@font-face {
|
||||
font-family: "Belgika";
|
||||
src: url("http://bohyewoo.com/webfonts/belgika/belgika-40th-webfont.eot");
|
||||
src: url("http://bohyewoo.com/webfonts/belgika/belgika-40th-webfont.woff") format("woff"),
|
||||
url("http://bohyewoo.com/webfonts/belgika/belgika-40th-webfont.svg#filename") format("svg");
|
||||
}
|
||||
|
||||
@font-face {
|
||||
font-family: "Belgika";
|
||||
src: url("http://bohyewoo.com/webfonts/belgika/belgika-16th-webfont.eot");
|
||||
src: url("http://bohyewoo.com/webfonts/belgika/belgika-16th-webfont.woff") format("woff"),
|
||||
url("http://bohyewoo.com/webfonts/belgika/belgika-16th-webfont.svg#filename") format("svg");
|
||||
}
|
||||
|
||||
@font-face {
|
||||
font-family: "Belgika";
|
||||
src: url("http://bohyewoo.com/webfonts/belgika/belgika-8th-webfont.eot");
|
||||
src: url("http://bohyewoo.com/webfonts/belgika/belgika-8th-webfont.woff") format("woff"),
|
||||
url("http://bohyewoo.com/webfonts/belgika/belgika-8th-webfont.svg#filename") format("svg");
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: helvetica;
|
||||
font-weight: regular;
|
||||
letter-spacing: 0.5px;
|
||||
font-size: 20px;
|
||||
line-height: 1.2;
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
.NNP {
|
||||
background-color: pink;
|
||||
}
|
||||
|
||||
.VBP {
|
||||
background-color: gold;
|
||||
}
|
||||
|
||||
.NN {
|
||||
background-color: LightSkyBlue;
|
||||
}
|
||||
|
||||
.NNS {
|
||||
background-color: Aquamarine;
|
||||
}
|
||||
|
||||
.paragraph {
|
||||
width: 70%;
|
||||
float: right;
|
||||
}
|
||||
|
||||
.top_words {
|
||||
font-size: 9pt;
|
||||
width: 25%;
|
||||
float: left;
|
||||
}
|
||||
|
||||
</style>
|
||||
</head>
|
||||
<body>''')
|
||||
|
||||
|
||||
# my stopwords are common words I don't want to count, like "a", "an", "the".
|
||||
|
||||
print('<div class ="paragraph">')
|
||||
for sentence in sent_tokenize(text):
|
||||
print('<span>')
|
||||
|
||||
tokenized = word_tokenize(sentence)
|
||||
tagged = pos_tag(tokenized)
|
||||
|
||||
# for HTML
|
||||
for word, pos in tagged:
|
||||
print('<span class="{}">{}</span>'.format(pos, word))
|
||||
|
||||
print('</span>')
|
||||
print('</div>')
|
||||
|
||||
# filtering stopwords
|
||||
tokens_without_stopwords = nltk.FreqDist(words.lower() for words in tokenized if words.lower() not in all_stopwords)
|
||||
print(tokens_without_stopwords)
|
||||
|
||||
# for read_whole_text in tokens_without_stopwords:
|
||||
# whole_text_tokenized =
|
||||
# print(whole_text_tokenized)
|
||||
|
||||
# #filtered words in sentence
|
||||
# filtered_sentence = (" ").join(tokens_without_stopwords)
|
||||
# print(filtered_sentence)
|
||||
|
||||
print('<div class="top_words"> colonial words:')
|
||||
|
||||
frequency_word = FreqDist(tokens_without_stopwords)
|
||||
top_words = tokens_without_stopwords.most_common(10)
|
||||
|
||||
for chosen_words, frequency in top_words:
|
||||
print('<br><span class="chosen_words">{}({}) </span>'.format(chosen_words, frequency))
|
||||
|
||||
|
||||
print('''</div></body></html>''')
|
||||
|
||||
|
||||
|
||||
# for new_file in tokens_without_stopwords:
|
||||
# appendFile = open('tokenized_words.txt', 'a')
|
||||
# appendFile.write(" " + new_file)
|
||||
# appendFile.close()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# #shows only stopwords
|
||||
# processed_word_list = []
|
||||
|
||||
# for word in tokenized:
|
||||
# # print(word)
|
||||
# if word not in all_stopwords:
|
||||
# processed_word_list.append('*')
|
||||
# else:
|
||||
# processed_word_list.append(word)
|
||||
# print(processed_word_list)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# # # result putting in a graph
|
||||
# top_words_plot = frequency_word.plot(10)
|
||||
# print(top_words_plot)
|
||||
Loading…
Reference in New Issue