本文整理汇总了Python中nltk.sentiment.SentimentAnalyzer.unigram_word_feats方法的典型用法代码示例。如果您正苦于以下问题:Python SentimentAnalyzer.unigram_word_feats方法的具体用法?Python SentimentAnalyzer.unigram_word_feats怎么用?Python SentimentAnalyzer.unigram_word_feats使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nltk.sentiment.SentimentAnalyzer
的用法示例。
在下文中一共展示了SentimentAnalyzer.unigram_word_feats方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
def train():
positive_tweets = read_tweets('/root/295/new/positive.txt', 'positive')
negative_tweets = read_tweets('/root/295/new/negative.txt', 'negative')
print len(positive_tweets)
print len(negative_tweets)
#pos_train = positive_tweets[:2000]
#neg_train = negative_tweets[:2000]
#pos_test = positive_tweets[2001:3000]
#neg_test = negative_tweets[2001:3000]
pos_train = positive_tweets[:len(positive_tweets)*80/100]
neg_train = negative_tweets[:len(negative_tweets)*80/100]
pos_test = positive_tweets[len(positive_tweets)*80/100+1:]
neg_test = negative_tweets[len(positive_tweets)*80/100+1:]
training_data = pos_train + neg_train
test_data = pos_test + neg_test
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_data])
#print all_words_neg
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
#print unigram_feats
print len(unigram_feats)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = sentim_analyzer.apply_features(training_data)
test_set = sentim_analyzer.apply_features(test_data)
print test_set
trainer = NaiveBayesClassifier.train
classifier = sentim_analyzer.train(trainer, training_set)
for key,value in sorted(sentim_analyzer.evaluate(test_set).items()):
print('{0}: {1}'.format(key, value))
print sentim_analyzer.classify(tokenize_sentance('I hate driving car at night'))
return sentim_analyzer
示例2: sentiment_analysis
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
def sentiment_analysis(self, testing_data, training_data=None):
if training_data is None:
training_data = self.training_data
## Apply sentiment analysis to data to extract new "features"
# Initialize sentiment analyzer object
sentiment_analyzer = SentimentAnalyzer()
# Mark all negative words in training data, using existing list of negative words
all_negative_words = sentiment_analyzer.all_words([mark_negation(data) for data in training_data])
unigram_features = sentiment_analyzer.unigram_word_feats(all_negative_words, min_freq=4)
len(unigram_features)
sentiment_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_features)
training_final = sentiment_analyzer.apply_features(training_data)
testing_final = sentiment_analyzer.apply_features(testing_data)
## Traing model and test
model = NaiveBayesClassifier.train
classifer = sentiment_analyzer.train(model, training_final)
for key, value in sorted(sentiment_analyzer.evaluate(testing_final).items()):
print ("{0}: {1}".format(key, value))
示例3: demo_subjectivity
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
def demo_subjectivity(trainer, save_analyzer=False, n_instances=None, output=None):
"""
Train and test a classifier on instances of the Subjective Dataset by Pang and
Lee. The dataset is made of 5000 subjective and 5000 objective sentences.
All tokens (words and punctuation marks) are separated by a whitespace, so
we use the basic WhitespaceTokenizer to parse the data.
:param trainer: `train` method of a classifier.
:param save_analyzer: if `True`, store the SentimentAnalyzer in a pickle file.
:param n_instances: the number of total sentences that have to be used for
training and testing. Sentences will be equally split between positive
and negative.
:param output: the output file where results have to be reported.
"""
from nltk.sentiment import SentimentAnalyzer
from nltk.corpus import subjectivity
if n_instances is not None:
n_instances = int(n_instances/2)
subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:n_instances]]
obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:n_instances]]
# We separately split subjective and objective instances to keep a balanced
# uniform class distribution in both train and test sets.
train_subj_docs, test_subj_docs = split_train_test(subj_docs)
train_obj_docs, test_obj_docs = split_train_test(obj_docs)
training_docs = train_subj_docs+train_obj_docs
testing_docs = test_subj_docs+test_obj_docs
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
# Add simple unigram word features handling negation
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
# Apply features to obtain a feature-value representation of our datasets
training_set = sentim_analyzer.apply_features(training_docs)
test_set = sentim_analyzer.apply_features(testing_docs)
classifier = sentim_analyzer.train(trainer, training_set)
try:
classifier.show_most_informative_features()
except AttributeError:
print('Your classifier does not provide a show_most_informative_features() method.')
results = sentim_analyzer.evaluate(test_set)
if save_analyzer == True:
save_file(sentim_analyzer, 'sa_subjectivity.pickle')
if output:
extr = [f.__name__ for f in sentim_analyzer.feat_extractors]
output_markdown(output, Dataset='subjectivity', Classifier=type(classifier).__name__,
Tokenizer='WhitespaceTokenizer', Feats=extr,
Instances=n_instances, Results=results)
return sentim_analyzer
示例4: demo_movie_reviews
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
def demo_movie_reviews(trainer, n_instances=None, output=None):
"""
Train classifier on all instances of the Movie Reviews dataset.
The corpus has been preprocessed using the default sentence tokenizer and
WordPunctTokenizer.
Features are composed of:
- most frequent unigrams
:param trainer: `train` method of a classifier.
:param n_instances: the number of total reviews that have to be used for
training and testing. Reviews will be equally split between positive and
negative.
:param output: the output file where results have to be reported.
"""
from nltk.corpus import movie_reviews
from nltk.sentiment import SentimentAnalyzer
if n_instances is not None:
n_instances = int(n_instances/2)
pos_docs = [(list(movie_reviews.words(pos_id)), 'pos') for pos_id in movie_reviews.fileids('pos')[:n_instances]]
neg_docs = [(list(movie_reviews.words(neg_id)), 'neg') for neg_id in movie_reviews.fileids('neg')[:n_instances]]
# We separately split positive and negative instances to keep a balanced
# uniform class distribution in both train and test sets.
train_pos_docs, test_pos_docs = split_train_test(pos_docs)
train_neg_docs, test_neg_docs = split_train_test(neg_docs)
training_docs = train_pos_docs+train_neg_docs
testing_docs = test_pos_docs+test_neg_docs
sentim_analyzer = SentimentAnalyzer()
all_words = sentim_analyzer.all_words(training_docs)
# Add simple unigram word features
unigram_feats = sentim_analyzer.unigram_word_feats(all_words, min_freq=4)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
# Apply features to obtain a feature-value representation of our datasets
training_set = sentim_analyzer.apply_features(training_docs)
test_set = sentim_analyzer.apply_features(testing_docs)
classifier = sentim_analyzer.train(trainer, training_set)
try:
classifier.show_most_informative_features()
except AttributeError:
print('Your classifier does not provide a show_most_informative_features() method.')
results = sentim_analyzer.evaluate(test_set)
if output:
extr = [f.__name__ for f in sentim_analyzer.feat_extractors]
output_markdown(output, Dataset='Movie_reviews', Classifier=type(classifier).__name__,
Tokenizer='WordPunctTokenizer', Feats=extr, Results=results,
Instances=n_instances)
示例5: train_model
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
def train_model(training):
## Apply sentiment analysis to data to extract new "features"
# Initialize sentiment analyzer object
sentiment_analyzer = SentimentAnalyzer()
# Mark all negative words in training data, using existing list of negative words
all_negative_words = sentiment_analyzer.all_words([mark_negation(data) for data in training])
unigram_features = sentiment_analyzer.unigram_word_feats(all_negative_words, min_freq=4)
len(unigram_features)
sentiment_analyzer.add_feat_extractor(extract_unigram_feats,unigrams=unigram_features)
training_final = sentiment_analyzer.apply_features(training)
return [training_final]
示例6: get_objectivity_analyzer
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
def get_objectivity_analyzer():
n_instances = 100
subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:n_instances]]
obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:n_instances]]
train_subj_docs = subj_docs
train_obj_docs = obj_docs
training_docs = train_subj_docs+train_obj_docs
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = sentim_analyzer.apply_features(training_docs)
trainer = NaiveBayesClassifier.train
sentiment_classifier = sentim_analyzer.train(trainer, training_set)
return sentim_analyzer
示例7: SentimentAnalyzer
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
subj_docs[0]
(['smart', 'and', 'alert', ',', 'thirteen', 'conversations', 'about', 'one',
'thing', 'is', 'a', 'small', 'gem', '.'], 'subj')
train_subj_docs = subj_docs[:80]
test_subj_docs = subj_docs[80:100]
train_obj_docs = obj_docs[:80]
test_obj_docs = obj_docs[80:100]
training_docs = train_subj_docs+train_obj_docs
testing_docs = test_subj_docs+test_obj_docs
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=1)
len(unigram_feats)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = sentim_analyzer.apply_features(training_docs)
test_set = sentim_analyzer.apply_features(testing_docs)
trainer = NaiveBayesClassifier.train
classifier = sentim_analyzer.train(trainer, training_set)
for key,value in sorted(sentim_analyzer.evaluate(test_set).items()):
print('{0}: {1}'.format(key, value))
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sentences = ["VADER is smart, handsome, and funny.", # positive sentence example
"VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted)
"VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted)
示例8: demo_tweets
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
def demo_tweets(trainer, n_instances=None, output=None):
"""
Train and test Naive Bayes classifier on 10000 tweets, tokenized using
TweetTokenizer.
Features are composed of:
- 1000 most frequent unigrams
- 100 top bigrams (using BigramAssocMeasures.pmi)
:param trainer: `train` method of a classifier.
:param n_instances: the number of total tweets that have to be used for
training and testing. Tweets will be equally split between positive and
negative.
:param output: the output file where results have to be reported.
"""
from nltk.tokenize import TweetTokenizer
from nltk.sentiment import SentimentAnalyzer
from nltk.corpus import twitter_samples, stopwords
# Different customizations for the TweetTokenizer
tokenizer = TweetTokenizer(preserve_case=False)
# tokenizer = TweetTokenizer(preserve_case=True, strip_handles=True)
# tokenizer = TweetTokenizer(reduce_len=True, strip_handles=True)
if n_instances is not None:
n_instances = int(n_instances/2)
fields = ['id', 'text']
positive_json = twitter_samples.abspath("positive_tweets.json")
positive_csv = 'positive_tweets.csv'
json2csv_preprocess(positive_json, positive_csv, fields, limit=n_instances)
negative_json = twitter_samples.abspath("negative_tweets.json")
negative_csv = 'negative_tweets.csv'
json2csv_preprocess(negative_json, negative_csv, fields, limit=n_instances)
neg_docs = parse_tweets_set(negative_csv, label='neg', word_tokenizer=tokenizer)
pos_docs = parse_tweets_set(positive_csv, label='pos', word_tokenizer=tokenizer)
# We separately split subjective and objective instances to keep a balanced
# uniform class distribution in both train and test sets.
train_pos_docs, test_pos_docs = split_train_test(pos_docs)
train_neg_docs, test_neg_docs = split_train_test(neg_docs)
training_tweets = train_pos_docs+train_neg_docs
testing_tweets = test_pos_docs+test_neg_docs
sentim_analyzer = SentimentAnalyzer()
# stopwords = stopwords.words('english')
# all_words = [word for word in sentim_analyzer.all_words(training_tweets) if word.lower() not in stopwords]
all_words = [word for word in sentim_analyzer.all_words(training_tweets)]
# Add simple unigram word features
unigram_feats = sentim_analyzer.unigram_word_feats(all_words, top_n=1000)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
# Add bigram collocation features
bigram_collocs_feats = sentim_analyzer.bigram_collocation_feats([tweet[0] for tweet in training_tweets],
top_n=100, min_freq=12)
sentim_analyzer.add_feat_extractor(extract_bigram_feats, bigrams=bigram_collocs_feats)
training_set = sentim_analyzer.apply_features(training_tweets)
test_set = sentim_analyzer.apply_features(testing_tweets)
classifier = sentim_analyzer.train(trainer, training_set)
# classifier = sentim_analyzer.train(trainer, training_set, max_iter=4)
try:
classifier.show_most_informative_features()
except AttributeError:
print('Your classifier does not provide a show_most_informative_features() method.')
results = sentim_analyzer.evaluate(test_set)
if output:
extr = [f.__name__ for f in sentim_analyzer.feat_extractors]
output_markdown(output, Dataset='labeled_tweets', Classifier=type(classifier).__name__,
Tokenizer=tokenizer.__class__.__name__, Feats=extr,
Results=results, Instances=n_instances)
示例9: SentimentAnalyzer
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
s2 = line
tup = (s1, s2)
train.append(tup)
i += 1
train_docs = train[:3359]
test_docs = train[3359:]
top_ns = [10, 20, 50, 100, 200, 300]
min_freqq = 4
for top_nn in top_ns:
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in train_docs])
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, top_n=top_nn, min_freq=min_freqq)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = sentim_analyzer.apply_features(train_docs)
testing_set = sentim_analyzer.apply_features(test_docs)
trainer = MaxentClassifier.train
classifierme = sentim_analyzer.train(trainer, training_set)
f = open('results/maxent/noemoticons/maxent_top_n_' + str(top_nn) + '_min_freq_' + str(min_freqq) + '-noemoticons.txt', 'w')
for key, value in sorted(sentim_analyzer.evaluate(testing_set, classifier=classifierme).items()):
print('{0}: {1}'.format(key, value))
f.write('{0}: {1}'.format(key, value))
f.close()
#f = open('maxent_trained_with_80_percent_2.pickle', 'wb')
#pickle.dump(classifierme, f)
示例10: SentimentAnalyzer
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
print "creating data set"
i = 0
s1 = ""
s2 = ""
tup = (s1, s2)
for line in f:
if i > 6718:
break
if i % 2 == 0:
s1 = line.split()
else:
s2 = line
tup = (s1, s2)
train.append(tup)
i += 1
print train
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in train])
print all_words_neg
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg)
print unigram_feats
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = sentim_analyzer.apply_features(train)
trainer = MaxentClassifier.train
classifier = sentim_analyzer.train(trainer, training_set)
f = open('maxent_trained_with_80_percent.pickle', 'wb')
pickle.dump(classifier, f)
f.close()
示例11: open
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
import nltk
from nltk.tokenize import word_tokenize
from nltk.classify import NaiveBayesClassifier
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
f = open("training_set.txt",'r')
sa = SentimentAnalyzer()
trainingset = []
for line in f:
senti = line.split(",")[0]
content = line[len(senti)+1:]
tokens = word_tokenize(content.rstrip())
trainingset.append((tokens,senti))
all_words_neg = sa.all_words([mark_negation(doc) for doc in trainingset])
unigram_feats = sa.unigram_word_feats(all_words_neg,min_freq = 4)
sa.add_feat_extractor(extract_unigram_feats,unigrams=unigram_feats)
training_set = sa.apply_features(trainingset)
for line in sys.stdin:
if "username" in line:
continue
tweetWords=[]
tweet= line.split(";")[4]
likes = line.split(";")[3]
likes = int(likes)
if likes==0:
num=1
else:
num = 1+likes
示例12: SentimentAnalyzer
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
# Now aggregate the training and test sets
training = training_subjective + training_objective
test = test_subjective + test_objective
## Apply sentiment analysis to data to extract new "features"
# Initialize sentiment analyzer object
sentiment_analyzer = SentimentAnalyzer()
# Mark all negative words in training data, using existing list of negative words
all_negative_words = sentiment_analyzer.all_words([mark_negation(data) for data in training])
unigram_features = sentiment_analyzer.unigram_word_feats(all_negative_words, min_freq=4)
len(unigram_features)
sentiment_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_features)
training_final = sentiment_analyzer.apply_features(training)
test_final = sentiment_analyzer.apply_features(test)
## Traing model and test
model = NaiveBayesClassifier.train
classifer = sentiment_analyzer.train(model, training_final)
for key, value in sorted(sentiment_analyzer.evaluate(test_final).items()):
print("{0}: {1}".format(key, value))
示例13: read_input
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
text = tokenizer.tokenize(line[5].decode("utf-8"))
text = [token for token in text if token != u'\ufffd']
test.append((text, sent))
return test, train
# Read in annotated data
NUM_TRAIN = 10000
NUM_TEST = 2500
test, train = read_input("train.csv",NUM_TRAIN,NUM_TEST)
sentiment_analyzer = SentimentAnalyzer()
#all_words = sentiment_analyzer.all_words([mark_negation(doc[0]) for doc in train])
all_words = sentiment_analyzer.all_words([doc[0] for doc in train])
unigrams = sentiment_analyzer.unigram_word_feats(all_words, min_freq=4)
# print unigrams
sentiment_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigrams)
training_set=sentiment_analyzer.apply_features(train)
test_set=sentiment_analyzer.apply_features(test)
trainer = NaiveBayesClassifier.train
classifier = sentiment_analyzer.train(trainer, training_set)
save_file(sentiment_analyzer, "sentiment_classifier.pkl")
for key,value in sorted(sentiment_analyzer.evaluate(test_set).items()):
print("{0}: {1}".format(key,value))
示例14: SentimentAnalyzer
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
sentim_analyzer = SentimentAnalyzer()
#get list of stopwords (with _NEG) to use as a filter
stopwords_all = []
for word in stopwords.words('english'):
stopwords_all.append(word)
stopwords_all.append(word + '_NEG')
#take 10,000 Tweets from this training dataset for this example and get all the words
#that are not stop words
train_data_sample = train_data.take(10000)
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in train_data_sample])
all_words_neg_nostops = [x for x in all_words_neg if x not in stopwords_all]
#create unigram features and extract features
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg_nostops, top_n=200)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = sentim_analyzer.apply_features(train_data_sample)
#train the model
trainer = NaiveBayesClassifier.train
classifier = sentim_analyzer.train(trainer, training_set)
#classify test sentences
test_sentence1 = [(['this', 'program', 'is', 'bad'], '')]
test_sentence2 = [(['tough', 'day', 'at', 'work', 'today'], '')]
test_sentence3 = [(['good', 'wonderful', 'amazing', 'awesome'], '')]
test_set = sentim_analyzer.apply_features(test_sentence1)
test_set2 = sentim_analyzer.apply_features(test_sentence2)
test_set3 = sentim_analyzer.apply_features(test_sentence3)
示例15: SuicideClassifier
# 需要导入模块: from nltk.sentiment import SentimentAnalyzer [as 别名]
# 或者: from nltk.sentiment.SentimentAnalyzer import unigram_word_feats [as 别名]
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)