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nltk_classify_unigrams.py
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nltk_classify_unigrams.py
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import nltk.classify.util
from nltk.classify import NaiveBayesClassifier
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
import pandas as pd
import urllib2
import pdb
import datetime
from vaderSentiment.vaderSentiment import sentiment as vaderSentiment
def filter_negative_phrases(phrases):
phrases_to_keep = {}
for phrase in phrases:
try:
req = urllib2.Request('https://japerk-text-processing.p.mashape.com/sentiment/', 'text=' + str(phrase))
req.add_header('X-Mashape-Key', 'aUisSbUwWqmshbye6c1UpIe9qxtep1LIHSjjsnI81LIi9gZmKR')
response = urllib2.urlopen(req)
result = eval(response.read())
if result['probability']['pos'] > .55:
print phrase, ': not negative. dropping'
elif result['probability']['neg'] > .5:
# print phrase, ': negative! keeping (score: ' +
# str(result['probability']['neg'] - result['probability']['pos']) + ')'
phrases_to_keep[phrase] = result['probability']['neg'] - result['probability']['pos']
# pdb.set_trace()
except Exception, e:
print e
return phrases_to_keep
def filter_positive_phrases(phrases):
phrases_to_keep = {}
for phrase in phrases:
try:
req = urllib2.Request('https://japerk-text-processing.p.mashape.com/sentiment/', 'text=' + str(phrase))
req.add_header('X-Mashape-Key', 'aUisSbUwWqmshbye6c1UpIe9qxtep1LIHSjjsnI81LIi9gZmKR')
response = urllib2.urlopen(req)
result = eval(response.read())
if result['probability']['pos'] > .55:
phrases_to_keep[phrase] = result['probability']['neg'] - result['probability']['pos']
elif result['probability']['neg'] < .5:
phrases_to_keep[phrase] = result['probability']['neg'] - result['probability']['pos']
# pdb.set_trace()
except Exception, e:
print e
return phrases_to_keep
def load_csv_sentences(filename):
df = pd.read_csv(filename)
df = df.text
phrases = []
for x in df:
phrases = phrases + x.replace(',', '.').replace('?', '.').replace('!', '.').replace('\n', '.').split('.')
phrases = [x.lower() for x in phrases if len(x) > 3 and len(x) < 200]
return phrases
def write_csv_files_with_vader():
for filename in ["neg_phrases_filtered.txt",
"pos_phrases_filtered.txt",
"neg_phrases.txt",
"pos_phrases.txt"]:
new_filename = 'vader_' + filename
with open(filename, "r") as file:
phrases = file.readlines()
with open(new_filename, 'w') as new_file:
for phrase in phrases:
vader_sent = vaderSentiment(str(phrase))
new_file.write(phrase[:-1] + ','
+ str(vader_sent['neg']) + ','
+ str(vader_sent['neu']) + ','
+ str(vader_sent['pos']) + ','
+ str(vader_sent['compound']) + '\n')
def write_webtext_csv():
from nltk.corpus import webtext
file_reader = webtext.open('overheard.txt')
phrases_to_track = []
with open('webtext_phrases_with_lots_of_words.txt', 'w') as file:
for line in file_reader:
if ':' in line:
try:
line = str(line.lower())
line = line[line.index(':') + 2:]
phrases = line.replace(',', '.').replace('?', '.').replace('!', '.').replace('\n', '.').split('.')
for phrase in phrases:
if len(phrase.split(' ')) > 3 and len(phrase) < 200:
file.write(phrase + '\n')
phrases_to_track.append(phrase)
except Exception:
pass
phrases_to_keep = filter_positive_phrases(phrases_to_track[::-1][:500])
with open('webtext_phrases_filtered.txt', 'w') as file:
for phrase in phrases_to_keep:
file.write(phrase + '\n')
def vader_sentiment_feat(document):
vader_sent = vaderSentiment(str(' '.join(document)))
vader_neg = vader_sent['neg']
vader_neu = vader_sent['neu']
vader_pos = vader_sent['pos']
# return {'vaderNeuOver.2': (vader_neu > .2), 'vaderNeuOver.5': (vader_neu > .5), 'vaderNeuOver.8': (vader_neu > .8),
# 'vaderNegOver.2': (vader_neg > .2), 'vaderNegOver.5': (vader_neg > .5), 'vaderNegOver.8': (vader_neg > .8),
# 'vaderPosOver.2': (vader_pos > .2), 'vaderPosOver.5': (vader_pos > .5), 'vaderPosOver.8': (vader_pos > .8)}
# return {'vaderNeuOver.2': (vader_neu >= .2 and vader_neu < .5), 'vaderNeuOver.5': (vader_neu >= .5 and vader_neu < .8), 'vaderNeuOver.8': (vader_neu >= .8),
# 'vaderNegOver.2': (vader_neg >= .2 and vader_neg < .5), 'vaderNegOver.5': (vader_neg >= .5 and vader_neg < .8), 'vaderNegOver.8': (vader_neg >= .8),
# 'vaderPosOver.2': (vader_pos >= .2 and vader_pos < .5), 'vaderPosOver.5': (vader_pos >= .5 and vader_pos < .8), 'vaderPosOver.8': (vader_pos >= .8)}
# return {'vaderNegScore': vader_neg, 'vaderNeuScore': vader_neu, 'vaderPosScore': vader_pos}
return {'vaderNeu': round(vader_neu * 10), 'vaderNeg': round(vader_neg * 10), 'vaderPos': round(vader_pos * 10)}
class SuicideClassifier(object):
def __init__(self, sentiment_only, num_phrases_to_track=20):
# neg_phrases = filter_negative_phrases(load_csv_sentences('thoughtsandfeelings.csv'))
# pos_phrases = filter_positive_phrases(load_csv_sentences('spiritualforums.csv'))
# file_pos = open("pos_phrases.txt", 'w')
# file_neg = open("neg_phrases.txt", 'w')
# for item in pos_phrases:
# print>>file_pos, item
# for item in neg_phrases:
# print>>file_neg, item
self.recent_sentiment_scores = []
neg_file = open("ALL_neg_phrases_filtered.txt", "r")
pos_file = open("webtext_phrases_with_lots_of_words.txt", "r")
neg_phrases = neg_file.readlines()
pos_phrases = pos_file.readlines()
neg_docs = []
pos_docs = []
for phrase in neg_phrases:
neg_docs.append((phrase.split(), 'suicidal'))
for phrase in pos_phrases[:len(neg_phrases)]:
pos_docs.append((phrase.split(), 'alright'))
print len(neg_docs)
print len(pos_docs)
# negcutoff = len(neg_docs) * 3 / 4
# poscutoff = len(pos_docs) * 3 / 4
negcutoff = -200
poscutoff = -200
train_pos_docs = pos_docs[:poscutoff]
test_pos_docs = pos_docs[poscutoff:]
train_neg_docs = neg_docs[:negcutoff]
test_neg_docs = neg_docs[negcutoff:]
training_docs = train_pos_docs + train_neg_docs
testing_docs = test_pos_docs + test_neg_docs
self.sentim_analyzer = SentimentAnalyzer()
if not sentiment_only:
all_words = self.sentim_analyzer.all_words([doc for doc in training_docs])
unigram_feats = self.sentim_analyzer.unigram_word_feats(all_words, min_freq=1)
self.sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
self.sentim_analyzer.add_feat_extractor(vader_sentiment_feat)
# bigram_feats = self.sentim_analyzer.bigram_collocation_feats(all_words, min_freq=1)
# self.sentim_analyzer.add_feat_extractor(extract_bigram_feats, bigrams=bigram_feats)
training_set = self.sentim_analyzer.apply_features(training_docs)
test_set = self.sentim_analyzer.apply_features(testing_docs)
trainer = NaiveBayesClassifier.train
self.classifier = self.sentim_analyzer.train(trainer, training_set)
for key, value in sorted(self.sentim_analyzer.evaluate(test_set).items()):
print('{0}: {1}'.format(key, value))
self.classifier.show_most_informative_features(20)
def test(self, phrase):
return self.sentim_analyzer.classify(phrase.split())
def update_sentiments(self, value):
now = datetime.datetime.now()
self.recent_sentiment_scores.append([now, value])
self.recent_sentiment_scores = [x for x in self.recent_sentiment_scores if x[
0] > now - datetime.timedelta(seconds=60)]
print sum([x[1] for x in self.recent_sentiment_scores]) / len(self.recent_sentiment_scores)
return sum([x[1] for x in self.recent_sentiment_scores]) / len(self.recent_sentiment_scores)
def main():
classifier = SuicideClassifier(False)
classifier_sentiment = SuicideClassifier(True)
test_string = ''
print 'Welcome!'
while str(test_string) not in ('q', 'quit', 'exit'):
test_string = raw_input('Enter phrase to test: ')
our_classifier_results = classifier.test(str(test_string))
sentiment_classifier_results = classifier_sentiment.test(str(test_string))
print('Our classifier says: ' + our_classifier_results)
print('Sentiment-only classifier says: ' + sentiment_classifier_results)
# vader_sent = vaderSentiment(str(test_string))
# print('Our classifier says: ' + our_classifier_results)
# print 'Vader says: ' + str(vader_sent)
# if classifier.update_sentiments(vader_sent['compound']) < -.3:
# if our_classifier_results == 'suicidal':
# print 'Please consider calling a hotline... I\'m worried about you...'
# else:
# print 'Hey, we can talk if you want to...'
print '\n'
if __name__ == '__main()__':
main()