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))
def train():
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[: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=4)
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))
def trainSubjectivity():
# Subjective vs. objective sentence classifier. Borrows from NLTK Documentation.
# Plan on using it in larger machine learning sentiment model as pre-processing
# Must differentiate between objective and subjective
subjDocs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')]
objDocs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')]
nSubj = len(subjDocs)
nObj = len(objDocs)
# 90% Training, 10% Test
subjTrain = int(.9 * nSubj)
objTrain = int(.9 * nObj)
trainSubj = subjDocs[:subjTrain]
testSubj = subjDocs[subjTrain:nSubj]
trainObj = objDocs[:objTrain]
testObj = objDocs[objTrain:nObj]
trainDocs = trainSubj + trainObj
testDocs = testSubj + testObj
# Create sentiment class, mark negation, create features (unigram)
sentiment = SentimentAnalyzer()
markNegation = sentiment.all_words([mark_negation(doc) for doc in trainDocs])
unigramFeats = sentiment.unigram_word_feats(markNegation, min_freq=4)
sentiment.add_feat_extractor(extract_unigram_feats, unigrams=unigramFeats)
training = sentiment.apply_features(trainDocs)
testing = sentiment.apply_features(testDocs)
# Train classifier
trainer = NaiveBayesClassifier.train
subjectivityClassifier = sentiment.train(trainer, training)
joblib.dump(subjectivityClassifier, 'subjectivity.pkl')
for key, value in sorted(sentiment.evaluate(testing).items()): print('{0}: {1}'.format(key, value))
'''
def __init__(self):
#document represented by a tuple (sentence,labelt)
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]]
#split subj and objinstances to keep a balanced uniform class distribution in both train and test sets.
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
#train classifier
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
#use 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 representations of our datasets
training_set = sentim_analyzer.apply_features(training_docs)
test_set = sentim_analyzer.apply_features(testing_docs)
self.trainer = NaiveBayesClassifier.train
self.classifier = sentim_analyzer.train(self.trainer, training_set)
for key,value in sorted(sentim_analyzer.evaluate(test_set).items()):
print('{0}: {1}'.format(key, value))
self.sid = SentimentIntensityAnalyzer()
def sentiment_classifier(df):
df = df.copy()
# prepping data
df = df[['txgot_binary', 'Convo_1']].dropna()
text_process_col = pre.process_corpus(np.asarray(df['Convo_1']), [])
txgot_col = np.asarray(df['txgot_binary'])
# turns into list of tuples (convo, label)
docs = list(zip(text_process_col, txgot_col))
shuffle(docs)
training_docs = docs[:int(len(docs) * 2 / 3)]
test_docs = docs[int(len(docs) * 2 / 3):]
# sentiment analyzer
sentim_analyzer = SentimentAnalyzer()
# simple unigram word features, handling negation
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)
# train classifier
training_set = sentim_analyzer.apply_features(training_docs)
test_set = sentim_analyzer.apply_features(test_docs)
trainer = NaiveBayesClassifier.train
classifier = sentim_analyzer.train(trainer, training_set)
# show results
for key, value in sentim_analyzer.evaluate(test_set).items():
print('{}: {}'.format(key, value))
def load_data(self, classifier=None):
# source: http://www.nltk.org/book/ch06.html, http://www.nltk.org/howto/sentiment.html
print "Loading training data...",
sys.stdout.flush()
training_docs, testing_docs = self.load_web_reviews()
# documents = [(word_tokenize(movie_reviews.raw(fileid)), category)
# for category in movie_reviews.categories()
# for fileid in movie_reviews.fileids(category)]
# random.shuffle(documents)
# cutoff = int(len(documents) * 0.1)
# training_docs, testing_docs = documents[cutoff:], documents[:cutoff]
print "Done!"
print "Extracting unigram features and applying to training data...",
sys.stdout.flush()
sentim_analyzer = SentimentAnalyzer(classifier=classifier)
all_words = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
unigram_feats = sentim_analyzer.unigram_word_feats(all_words)#, top_n=5000)
# print len(unigrams)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats, handle_negation=True)
training_set = sentim_analyzer.apply_features(training_docs)
testing_set = sentim_analyzer.apply_features(testing_docs)
print "Done!"
return sentim_analyzer, training_set, testing_set
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
def runSentanal(train, test):
sentanal = SentimentAnalyzer()
all_words_neg = sentanal.all_words([mark_negation(doc) for doc in train])
unigramFeats = sentanal.unigram_word_feats(all_words_neg, min_freq=4)
sentanal.add_feat_extractor(extract_unigram_feats,
unigrams=unigramFeats,
handle_negation=True)
# bigramFeats = sentanal.
# sentanal.add_feat_extractor(extract_bigram_feats, bigrams=bigramFeats)
trainList = sentanal.apply_features(train)
testList = sentanal.apply_features(test)
trainer = NaiveBayesClassifier.train
classifier = sentanal.train(trainer, trainList)
classifier.show_most_informative_features()
# creates array for storing values
values = []
# display results
for key, value in sorted(sentanal.evaluate(testList).items()):
print('{0}: {1}'.format(key, value))
values.append(value)
# write results to csv
with open(OUTPUT_CSV, mode='a') as csvFile:
writer = csv.writer(csvFile, delimiter=',')
writer.writerow(values)
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
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
def addfeatures(cleaned_tokens_list):
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words(
[mark_negation(token_list) for token_list in cleaned_tokens_list])
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg,
min_freq=4)
sentim_analyzer.add_feat_extractor(extract_unigram_feats,
unigrams=unigram_feats)
def GetSampleTrainDataForNLTK(self, trainSet):
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words(
[mark_negation(doc) for doc in trainSet])
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg,
min_freq=4)
sentim_analyzer.add_feat_extractor(extract_unigram_feats,
unigrams=unigram_feats)
sampleTrainData = sentim_analyzer.apply_features(trainSet)
return sampleTrainData
def main():
x, y = load_datasets(["../datasets/sentiment_uci/yelp_labelled.txt"])
stopwords = set()
with open('../stopwords.txt', 'r') as f:
for w in f:
stopwords.add(w.strip())
tok = TweetTokenizer()
x = [remove_stopwords(tok.tokenize(s.lower()), stopwords) for s in x]
x = np.array(x)
accumulate = dict()
folds = 10
for train_idx, test_idx in StratifiedKFold(y=y,
n_folds=folds,
shuffle=True):
train_x, train_y = x[train_idx], y[train_idx]
test_x, test_y = x[test_idx], y[test_idx]
# train_x = [remove_stopwords(tok.tokenize(s), stopwords) for s in train_x]
# test_x = [remove_stopwords(tok.tokenize(s), stopwords) for s in test_x]
train_docs = [(sent, label) for sent, label in zip(train_x, train_y)]
test_docs = [(sent, label) for sent, label in zip(test_x, test_y)]
cls = SentimentAnalyzer()
# train
words_with_neg = cls.all_words([mark_negation(a) for a in train_x])
unigram_feats = cls.unigram_word_feats(words_with_neg)
# bigram_feats = cls.bigram_collocation_feats(train_x)
cls.add_feat_extractor(extract_unigram_feats,
unigrams=unigram_feats,
handle_negation=True)
# cls.add_feat_extractor(extract_bigram_feats, bigrams=bigram_feats)
training_set = cls.apply_features(train_docs, labeled=True)
cls.train(MaxentClassifier.train, training_set, max_iter=10, trace=0)
# test & evaluate
test_set = cls.apply_features(test_docs)
for key, value in sorted(cls.evaluate(test_set).items()):
print('\t{0}: {1}'.format(key, value))
accumulate.setdefault(key, 0.0)
accumulate[key] += value if value is not None else 0.0
print("Averages")
for key, value in sorted(accumulate.items()):
print('\tAverage {0}: {1}'.format(key, value / folds))
def train_lr(training_set):
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_set])
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_set)
trainer = logreg.train
classifier = sentim_analyzer.train(trainer, training_set)
return [sentim_analyzer,classifier]
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)
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)
def sentiment_analysis(data):
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import subjectivity
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
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[: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=4)
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
from nltk import tokenize
sid = SentimentIntensityAnalyzer()
for line in data:
ss = sid.polarity_scores(line['line_text'])
line['compound'] = ss['compound']
line['neg'] = ss['neg']
line['pos'] = ss['pos']
line['neu'] = ss['neu']
def train_sentiment():
instances = 8000
subj = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:instances]]
obj = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:instances]]
train_subj = subj
train_obj = obj
train_set = train_subj + train_obj
sentiment = SentimentAnalyzer()
all_neg = sentiment.all_words([mark_negation(doc) for doc in train_set])
uni_g = sentiment.unigram_word_feats(all_neg, min_freq=4)
sentiment.add_feat_extractor(extract_unigram_feats, unigrams=uni_g)
trained_set = sentiment.apply_features(train_set)
nb = NaiveBayesClassifier.train
classifier = sentiment.train(nb, trained_set)
return classifier
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]
def subjectivity_classifier():
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import subjectivity
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
"""
Initializes and trains categorical subjectivity 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[:80]
test_subj_docs = subj_docs[80:]
train_obj_docs = obj_docs[:80]
test_obj_docs = obj_docs[80:]
training_docs = train_subj_docs + train_obj_docs
testing_docs = test_subj_docs + test_obj_docs
sent_analyzer = SentimentAnalyzer()
all_words_neg = sent_analyzer.all_words(
[mark_negation(doc) for doc in training_docs])
unigram_feats = sent_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
print(f"unigram feats: {len(unigram_feats)}")
sent_analyzer.add_feat_extractor(extract_unigram_feats,
unigrams=unigram_feats)
training_set = sent_analyzer.apply_features(training_docs)
test_set = sent_analyzer.apply_features(testing_docs)
trainer = NaiveBayesClassifier.train
classifier = sent_analyzer.train(trainer, training_set)
for k, v in sorted(sent_analyzer.evaluate(test_set).items()):
print(f"{k}: {v}")
return sent_analyzer
def get_nltk_NB(NEG_DATA, POS_DATA, num_train):
train_neg, test_neg = get_nltk_train_test(NEG_DATA, 'neg', num_train)
train_pos, test_pos = get_nltk_train_test(POS_DATA, 'pos', num_train)
training_docs = train_neg + train_pos
testing_docs = test_neg + test_pos
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)
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)
#results = []
for key,value in sorted(sentim_analyzer.evaluate(test_set).items()):
print('{0}: {1}'.format(key,value))
def analyze_sentiment(paragraph):
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[: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=4)
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)
sid = SentimentIntensityAnalyzer()
total_sum = 0
count = 0.0
sentences = sent_tokenize(paragraph)
for sentence in sentences:
total_sum += sid.polarity_scores(sentence)["compound"]
count += 1
return total_sum * 10 / count
def run_sa_twitt(train, test):
a = SentimentAnalyzer()
tr = NaiveBayesClassifier.train
all_words = [word for word in a.all_words(train)]
# Add simple unigram word features
unigram_feats = a.unigram_word_feats(all_words, top_n=1000)
a.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
# Add bigram collocation features
bigram_collocs_feats = a.bigram_collocation_feats(
[tweet[0] for tweet in train_twitt], top_n=100, min_freq=12)
a.add_feat_extractor(extract_bigram_feats, bigrams=bigram_collocs_feats)
tr_set = a.apply_features(train)
test_set = a.apply_features(test)
#Training
clf = a.train(tr, tr_set)
res = a.evaluate(test_set)
print(res)
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
def getTrainer():
if (LOAD == False):
dic = getData("/home/cioni/git/sentimentw/inputFolder/positive.csv",
"/home/cioni/git/sentimentw/inputFolder/negative.csv",
5000)
train = dic
snt = SentimentAnalyzer()
wrds = snt.all_words(dic, True)
feat = snt.unigram_word_feats(wrds, min_freq=3)
snt.add_feat_extractor(nltk.sentiment.util.extract_unigram_feats,
unigrams=feat)
train = snt.apply_features(train)
trainer = NaiveBayesClassifier.train
classifier = snt.apply_features(train, True)
snt.train(trainer, train)
clFile = open("classifierSmall2.pickle", "wb+")
pickle.dump(snt, clFile)
return snt
else:
load_cls = open("classifier.pickle", "rb")
snt = pickle.load(load_cls)
return snt
def prepare_review(review):
global word_counts
global review_counter
review_counter += 1
print(review_counter)
review = review.lower()
review = word_tokenize(review)
s_a = SentimentAnalyzer()
#this marks words between a negation phrase (ex, 'not'') and the next punctuation with a 'NEG' tag
#review = s_a.all_words([mark_negation(review)])
review = [word for word in review if word] # remove empty words
# For some reason, word lemmatizers have a difficult time handling the word 'hate' and think
# that it stems from the word 'hat'. So, you have to manually
review = [
word if word[:4] != 'hate' or word[:5] != 'hatin' else 'hate'
for word in review
]
pos_tags = pos_tag(review)
review = [
WordNetLemmatizer().lemmatize(word[0], get_wordnet_pos(word[1]))
for word in pos_tags
]
review = s_a.all_words([mark_negation(review)])
review = [
word for word in review if word not in stopwords.words('english')
]
# https://stackoverflow.com/questions/5843518/remove-all-special-characters-punctuation-and-spaces-from-string
review = [re.sub(r'[^(a-zA-Z\s]', '', word) for word in review]
review = [word for word in review if len(word) > 1]
for word in review:
if word in word_counts:
word_counts[word] += 1
else:
word_counts[word] = 1
review = ' '.join(review)
return review
neg_data['tweet'] = negative_json
neg_data['senti'] = 'neg'
result = pd.concat([pos_data, neg_data])
result = result.sample(frac=1).reset_index(drop=True)
#print result
training_tweets, testing_tweets = split_train_test(result)
#x_train, x_test, y_train, y_test = train_test_split(result['tweet'], result['senti'], test_size=0.20, random_state=0)
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]
print(all_words)
# 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)
def get_tweets(self, query, count=10):
tweets = []
try:
#get the tweets from twitter
fetched_tweets = self.api.search(q=query, count=count)
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[: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
emotion_analyzer = SentimentAnalyzer()
#get the negative words for feature extraction
all_radical_slurs = emotion_analyzer.all_words(
[mark_negation(doc) for doc in training_docs])
unigram_feats = emotion_analyzer.unigram_word_feats(
all_radical_slurs, min_freq=4)
emotion_analyzer.add_feat_extractor(extract_unigram_feats,
unigrams=unigram_feats)
training_set = emotion_analyzer.apply_features(training_docs)
test_set = emotion_analyzer.apply_features(testing_docs)
trainer = NaiveBayesClassifier.train
classifier = emotion_analyzer.train(trainer, training_set)
#test sentences
sentences = [
"Ravi is the worst boy in class",
"The story is full of mean bitchy characters",
"I had a good day!", "The day was okay",
"The day was very bad", "Harry potter is a good book",
"New Tata electric car is a piece of shit",
"It has been a long time since I had a good food",
"Stop acting as a asshole"
]
sid = SentimentIntensityAnalyzer()
for sentence in sentences:
print(sentence)
ss = sid.polarity_scores(sentence)
for k in sorted(ss):
print('{0}: {1}, '.format(k, ss[k]), end='')
print()
for tweet in fetched_tweets:
print(tweet.text)
ss = sid.polarity_scores(tweet.text)
for k in sorted(ss):
print('{0}: {1}, '.format(k, ss[k]), end='')
print()
return tweets
except tweepy.TweepError as e:
print("Error : " + str(e))
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)
objective_sentences = [
(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:instances]
]
# Divied each dataset into 20% test, 80% train
train_subjective = subjective_sentences[:80]
test_subjective = subjective_sentences[80:]
train_objective = objective_sentences[:80]
test_objective = objective_sentences[80:]
training_docs = train_objective + train_subjective
testing_docs = test_objective + test_subjective
analyzer = SentimentAnalyzer()
negative_words = analyzer.all_words(
[mark_negation(doc) for doc in training_docs])
features = analyzer.unigram_word_feats(negative_words, min_freq=4)
analyzer.add_feat_extractor(extract_unigram_feats, unigrams=features)
training_set = analyzer.apply_features(training_docs)
test_set = analyzer.apply_features(testing_docs)
## Training the classifier
trainer = NaiveBayesClassifier.train
classifier = analyzer.train(trainer, training_set)
for key, value in sorted(analyzer.evaluate(test_set).items()):
print('{0}: {1}'.format(key, value))
print("NLP classifier ready")
return math.log(len(bloblist) / (1 + n_containing(word, bloblist)))
def tfidf(word, blob, bloblist):
return tf(word, blob) * idf(word, bloblist)
bloblist = [text for text in df.head(100)['body']]
for i, blob in enumerate(bloblist):
print("Top words in document {}".format(i + 1))
scores = {word: tfidf(word, blob, bloblist) for word in blob.words}
sorted_words = sorted(scores.items(), key=lambda x: x[1], reverse=True)
for word, score in sorted_words[:3]:
print("\tWord: {}, TF-IDF: {}".format(word, round(score, 5)))
from nltk.sentiment import SentimentAnalyzer
sid = SentimentAnalyzer()
for sentence in bloblist:
print(sentence)
ss = sid.polarity_scores(sentence)
for k in sorted(ss):
print('{0}: {1}, '.format(k, ss[k]), end='')
print()
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([doc for doc in bloblist])
tokens = df['tokens'][2]
tokens
tagged = nltk.pos_tag(tokens)
test_objective = objective[int(0.8 * n) : n]
# 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))
import re
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:
# In[13]:
# Obtém a lista de stopwords em Inglês
stopwords_all = []
for word in stopwords.words('english'):
stopwords_all.append(word)
stopwords_all.append(word + '_NEG')
# In[14]:
# Obtém 10.000 tweets do dataset de treino e retorna todas as palavras que não são stopwords
dataset_treino_amostra = dataset_treino.take(10000)
# In[15]:
all_words_neg = sentiment_analyzer.all_words(
[mark_negation(doc) for doc in dataset_treino_amostra])
all_words_neg_nostops = [x for x in all_words_neg if x not in stopwords_all]
# In[16]:
# Cria um unigram (n-grama: sequência de palavras) e extrai as features / tri-grama wordtrueback deep learning
unigram_feats = sentiment_analyzer.unigram_word_feats(all_words_neg_nostops,
top_n=200)
sentiment_analyzer.add_feat_extractor(extract_unigram_feats,
unigrams=unigram_feats)
training_set = sentiment_analyzer.apply_features(dataset_treino_amostra)
# In[17]:
type(training_set)
(100, 100)
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)
class SentimentAnalyzerTry(object):
def __init__(self):
self.n_instances = 1000
self.n_training = int(self.n_instances * 0.8)
self.n_testing = int(self.n_instances * 0.2)
self.sentim_analyzer = SentimentAnalyzer()
def prepare_training_and_test_data(self):
"""
Each document is represented by a tuple (sentence, label). The sentence is tokenized, so it is represented by a list of strings.
E.g: (['smart', 'and', 'alert', ',', 'thirteen', 'conversations', 'about', 'one',
'thing', 'is', 'a', 'small', 'gem', '.'], 'subj')
"""
subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:self.n_instances]]
obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:self.n_instances]]
# We separately split subjective and objective instances to keep a balanced uniform class distribution in both train and test sets.
training_end = self.n_training
testing_start = training_end
testing_end = testing_start + self.n_testing
train_subj_docs = subj_docs[:training_end]
test_subj_docs = subj_docs[testing_start:testing_end]
train_obj_docs = obj_docs[:training_end]
test_obj_docs = obj_docs[testing_start:testing_end]
self.training_docs = train_subj_docs + train_obj_docs
self.testing_docs = test_subj_docs + test_obj_docs
def extract_training_test_features(self):
# We use simple unigram word features, handling negation.
self.all_words_neg = self.mark_negative_sentence(self.training_docs)
self.unigram_feats = self.sentim_analyzer.unigram_word_feats(self.all_words_neg, min_freq=4)
self.sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=self.unigram_feats)
# We apply features to obtain a feature-value representation of our datasets.
self.training_set = self.sentim_analyzer.apply_features(self.training_docs)
self.test_set = self.sentim_analyzer.apply_features(self.testing_docs)
def mark_negative_sentence(self, docs):
all_words_neg = self.sentim_analyzer.all_words([mark_negation(doc) for doc in docs])
return all_words_neg
def train_sentiment_analyzer(self, evaluate=True):
self.prepare_training_and_test_data()
self.extract_training_test_features()
# We can now train our classifier on the training set, and subsequently output the evaluation results
self.trainer = NaiveBayesClassifier.train
self.classifier = self.sentim_analyzer.train(self.trainer, self.training_set)
if evaluate:
self.evaluate_classifier()
def evaluate_classifier(self):
for key, value in sorted(self.sentim_analyzer.evaluate(self.test_set).items()):
print('{0}: {1}'.format(key, value))
def classify_text(self, text):
self.sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=self.unigram_feats)
return self.classifier.classify(self.sentim_analyzer.extract_features(tokenize.word_tokenize(text)))
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)
#call the function on each row in the dataset
train_data = train_data_raw.map(lambda line: get_row(line))
#create a SentimentAnalyzer object
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'], '')]
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))
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)
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]]
len(subj_docs), len(obj_docs)
subj_docs[0]
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=4)
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))
sid = SentimentIntensityAnalyzer()
auth = OAuthHandler(ckey, csecret)
training_ratio = 0.80
pos_ratio = int(training_ratio * len(pos_reviews))
neg_ratio = int(training_ratio * len(neg_reviews))
# partitioning the docs into training and testing
training_docs = pos_docs[:pos_ratio] + neg_docs[:neg_ratio]
testing_docs = pos_docs[pos_ratio:] + neg_docs[neg_ratio:]
print "preparing the classifier..."
# fetching all the words which will make the most_frequent_features.txt
# all_words_neg = sentiment_analyzer.all_words([w for (w,p) in training_docs])
# distributing words based on frequency
word_features = nltk.FreqDist(
sentiment_analyzer.all_words([w for (w, p) in training_docs]))
# then taking the values keys which are most frequent
word_features = sorted(word_features.items(),
key=operator.itemgetter(1))[-2000:]
word_features = [x for (x, y) in word_features]
# generating the feature set based on the word_features
feature_set = [(document_features(d, word_features), c)
for (d, c) in training_docs]
print "training..."
# selecting and training the NaiveBayesClassifier from nltk packages
classifier = NaiveBayesClassifier.train(feature_set)
class QuoteFinder:
def __init__(self):
self.sentim_analyzer = SentimentAnalyzer()
self.genre_dict = read_file("jsons/movie_genre_quote_dict_2.json")
context_file = "jsons/final_context.json"
movie_file = "jsons/final_movies.json"
quote_file = "jsons/final_quotes.json"
year_rating_file = "jsons/final_year_rating.json"
self.context = read_file(context_file)
self.movies = read_file(movie_file)
self.quotes = read_file(quote_file)
self.year_rating_dict = read_file(year_rating_file)
# Reincode to unicode
for i in range(len(self.context)):
self.context[i] = self.context[i].encode("utf-8").decode("utf-8")
self.movies[i] = self.movies[i].encode("utf-8").decode("utf-8")
self.quotes[i] = self.quotes[i].encode("utf-8").decode("utf-8")
self.context, self.quotes, self.movies = quote_pruner(self.context, self.quotes, self.movies)
self.inverted_index = read_file("jsons/f_inverted_index.json")
self.idf = read_file("jsons/f_idf.json")
# Initialize query tokenizer
self.tokenizer = TreebankWordTokenizer()
# Compute document norms
self.norms = compute_doc_norms(self.inverted_index, self.idf, len(self.context))
word_co_filename = "jsons/word_co.json"
word_count_filename = "jsons/word_count_dict.json"
pmi_dict_filename = "jsons/pmi_dict.json"
# Read files
self.word_co = read_file(word_co_filename)
self.word_count_dict = read_file(word_count_filename)
self.pmi_dict = read_file(pmi_dict_filename)
def find_basic_cooccurence(self, word_list):
""" Initialize the base word co-occurrance list from our context and quotes.
Arguments
=========
word_list: the list of words which are in our movie space
Returns
=======
word_co : a dictionary representing the word_occurrance matrix
"""
# Get English stop words
stop_words = stopwords.words('english')
# Merge context and quotes
quote_list = self.quotes
new_quote_list = []
for q in quote_list:
new_q = punct_strip(q)
if new_q not in self.context:
new_quote_list.append(new_q)
context_quotes = self.context + new_quote_list
# Find co occurences in context data, based co-occurences in a document
word_co = defaultdict(list)
word_count_dict = defaultdict(int)
for doc in context_quotes:
# Double loop to count word co-occurences
tkns = self.tokenizer.tokenize(doc)
for i in range(len(tkns)):
if tkns[i] not in stop_words:
word_count_dict[tkns[i]] += 1
for j in range(len(tkns)):
if not (j == i) and (tkns[j] in word_list):
word_co[tkns[i]] = update_word_counts(word_co[tkns[i]], tkns[j])
return word_co, word_count_dict
def update_cooccurence(self, word_co_old, word_count_dict_old, word_list, docs):
""" Updates the word co-occurrance mat and the word count dict with a new set of data.
Arguments
=========
word_co_old: a word co-occurrance matrix in the form of a dictionary
word_count_dict_old: a dictionary that keeps track of the total occurences of a word
word_list: the list of words which are in our movie space
docs: a list of new docs we're using to update our word co-occurence
Returns
=======
word_co, word_count_dict : new word co-occurence dict/mat and new word count dictionary
"""
# Get English stop words
stop_words = stopwords.words('english')
# Make init dict
word_co = defaultdict(list)
word_count_dict = defaultdict(int)
word_co.update(word_co_old)
word_count_dict.update(word_count_dict_old)
# Find co occurences in context data, based on document (content)
for doc in docs:
# Double loop to count word co-occurences
tkns = self.tokenizer.tokenize(punct_strip(doc))
for i in range(len(tkns)):
if tkns[i] not in stop_words:
word_count_dict[tkns[i]] += 1
for j in range(len(tkns)):
if not (j == i) and (tkns[j] in word_list):
word_co[tkns[i]] = update_word_counts(word_co[tkns[i]], tkns[j])
return word_co, word_count_dict
def query_vectorize(self, q, sw=False):
# Remove punctuation, lowercase, and encode to utf
query = punct_strip(q.lower().encode("utf-8").decode("utf-8"))
# Tokenize query and check query stopword cutoff
query_words = self.tokenizer.tokenize(query)
# Remove stop words if necessary
stop_words = stopwords.words('english') # Get English stop words
if (sw):
new_query = []
for x in query_words:
if x not in stop_words:
new_query.append(x)
query_words = new_query
# Make query tfidf
query_tfidf = defaultdict(int)
for word in query_words:
query_tfidf[word] += 1
for word in query_tfidf:
if word in self.idf:
query_tfidf[word] *= self.idf[word]
else:
query_tfidf[word] = 0
# Find query norm
query_norm = 0
for word in query_tfidf:
query_norm += math.pow(query_tfidf[word], 2)
query_norm = math.sqrt(query_norm)
return query_tfidf, query_norm
def pseudo_rocchio(self, query_tfidf, query_norm, relevant, sw=False, a=.3, b=.4, clip=True):
"""
Arguments:
query: a string representing the name of the movie being queried for
relevant: a list of int representing the indices of relevant movies for query
irrelevant: a list of strings representing the names of irrelevant movies for query
a,b: floats, corresponding to the weighting of the original query, relevant queriesrespectively.
clip: boolean, whether or not to clip all returned negative values to 0
Returns:
q_mod: a dict representing the modified query vector. this vector should have no negatve
weights in it!
"""
relevant_id = []
for s, i in relevant:
relevant_id.append(i)
if query_norm == 0:
return self.find_random()
# Calculate alpha*query_vec
query_vec = query_tfidf
for word in query_vec:
query_vec[word] /= query_norm
query_vec[word] *= a
# Get words in relevant docs
relevant_words = []
relevant_context = []
for i in relevant_id:
relevant_context.append(self.context[i])
for context in relevant_context:
context_tkns = self.tokenizer.tokenize(context)
for tkn in context_tkns:
if tkn not in relevant_words:
relevant_words.append(tkn)
# Collect relevant doc vector sums
relevant_docs = defaultdict(int)
for word in relevant_words:
if word in self.inverted_index:
for quote_id, tf in self.inverted_index[word]:
if quote_id in relevant_id:
relevant_docs[word] += (tf / self.norms[quote_id])
# Calculate beta term
beta_term = b * (1.0 / len(relevant))
for key in relevant_docs:
relevant_docs[key] *= beta_term
# Sum query and relevant
q_mod = {k: query_vec.get(k, 0) + relevant_docs.get(k, 0.0) for k in set(query_vec) | set(relevant_docs)}
# negative checks for terms, if clip
if (clip):
for key in q_mod:
if q_mod[key] < 0:
q_mod[key] = 0
return q_mod
else:
return q_mod
def find_random(self):
r = random.randint(0, len(self.quotes))
return [[self.quotes[r], self.movies[r], self.context[r]]]
def find_similar(self, query):
query_words = self.tokenizer.tokenize(query)
query_tfidf = defaultdict(int)
for word in query_words:
query_tfidf[word] += 1
for word in query_tfidf:
if word in self.idf:
query_tfidf[word] *= self.idf[word]
else:
query_tfidf[word] = 0
query_norm = 0
for word in query_tfidf:
query_norm += math.pow(query_tfidf[word], 2)
query_norm = math.sqrt(query_norm)
if query_norm == 0:
return self.find_random()
scores = [0 for _ in self.quotes]
for word in query_tfidf:
if word in self.inverted_index:
for quote_id, tf in self.inverted_index[word]:
scores[quote_id] += query_tfidf[word] * tf * self.idf[word]
results = []
for i, s in enumerate(scores):
if self.norms[i] != 0:
results.append((s / (self.norms[i] * query_norm), i))
top_res_num = 5
results.sort(reverse=True)
return [[self.quotes[i], self.movies[i], self.context[i]] for _, i in results[:top_res_num]]
def find_final(self, q, rocchio=True, pseudo_rocchio_num=5, sw=False, pmi_num=8, ml=False):
"""
Arguments:
q: a string representing the query
rocchio: a boolean representing whether or not to use pseudo relevance feedback with Rocchio
psudo_rocchio_num: and int representing the number of top documents to consider relevant for rocchio
sw: a boolean on whether or not to include stop words.
pmi_num: an int representing the number of items to add to the query to expand it with PMI.
Returns:
result_quotes: a list of the top x results
"""
# Vectorize query
query_tfidf, query_norm = self.query_vectorize(q, sw)
if query_norm == 0:
r = random.randint(0, len(self.quotes))
return [[self.quotes[r], self.movies[r], self.context[r]]]
# Expand query using PMI
# http://www.jofcis.com/publishedpapers/2011_7_1_17_24.pdf
pmi_expansion = defaultdict(float)
pmi_norm = 1
for word in query_tfidf: # Sum PMI lists
if word in self.pmi_dict.keys():
pmi_list = self.pmi_dict[word][:pmi_num]
pmi_score_list = []
for word, score in pmi_list:
pmi_expansion[word] += score
pmi_score_list.append(score)
temp_norm = 0
for s in pmi_score_list:
temp_norm += math.pow(s, 2)
temp_norm = math.sqrt(query_norm)
pmi_norm *= temp_norm
query_tfidf.update(pmi_expansion)
query_norm = query_norm * 2 * pmi_num * pmi_norm
# Find query norm
query_norm = 0
for word in query_tfidf:
query_norm += math.pow(query_tfidf[word], 2)
query_norm = math.sqrt(query_norm)
# Get scores
scores = [0 for _ in self.quotes]
for word in query_tfidf:
if word in self.inverted_index:
for quote_id, tf in self.inverted_index[word]:
scores[quote_id] += query_tfidf[word] * tf * self.idf[word]
results = []
for i, s in enumerate(scores):
if self.norms[i] != 0:
results.append((s / (self.norms[i] * query_norm), i))
# Weight scores with year and rating
for i in range(len(results)):
score = results[i][0]
index = results[i][1]
year = self.year_rating_dict[self.movies[i]][0]
rating = self.year_rating_dict[self.movies[i]][1]
results[i] = (year_rating_weight(float(year), float(rating), score), index)
# sort results
results.sort(reverse=True)
if rocchio:
# Do pseudo-relevance feedback with Rocchio
mod_query = self.pseudo_rocchio(query_tfidf, query_norm, results[:pseudo_rocchio_num], sw)
mod_query_norm = 0
for word in mod_query:
mod_query_norm += math.pow(mod_query[word], 2)
mod_query_norm = math.sqrt(mod_query_norm)
# Re-find scores and reweight with year and rating
scores = [0 for _ in self.quotes]
for word in mod_query:
if word in self.inverted_index:
for quote_id, tf in self.inverted_index[word]:
scores[quote_id] += mod_query[word] * tf * self.idf[word]
results = []
for i, s in enumerate(scores):
if self.norms[i] != 0:
results.append((s / (self.norms[i] * mod_query_norm), i))
d_score_updates = {}
if ml is True:
d_score_updates = self.find_ml(q)
# Weight scores with year and rating
for i in range(len(results)):
score = results[i][0]
index = results[i][1]
year = self.year_rating_dict[self.movies[i]][0]
rating = self.year_rating_dict[self.movies[i]][1]
results[i] = (year_rating_weight(float(year), float(rating), score), index)
if ml is True and index in d_score_updates:
results[i] = (results[i][0]*0.9 + d_score_updates[index], results[i][1])
# Sort and return results
top_res_num = 5
results.sort(reverse=True)
used_quotes = []
return_res = []
counter = 0
while len(return_res) <= top_res_num: # Avoid duplicate quotes
score, i = results[counter]
if self.quotes[i] not in used_quotes:
used_quotes.append(self.quotes[i])
return_res.append((score, i))
else:
counter += 1
result_quotes = [[self.quotes[i], self.movies[i], self.context[i]] for _, i in
return_res[:top_res_num]]
return result_quotes
def sentiment_analysis(self, td):
with open('jsons/all_words_neg.pickle', 'rb') as f:
all_words_neg = pickle.load(f)
with open('jsons/training_docs.pickle', 'rb') as f:
training_docs = pickle.load(f)
genres = ['action', 'crime', 'comedy', 'drama']
testing_docs = [(td, genre) for genre in genres]
all_words_neg = self.sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
unigram_feats = self.sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
all_words_neg = self.sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
self.sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = self.sentim_analyzer.apply_features(training_docs)
test_set = self.sentim_analyzer.apply_features(testing_docs)
trainer = NaiveBayesClassifier.train
classifier = self.sentim_analyzer.train(trainer, training_set)
# f = open('my_classifier_test.pickle', 'rb')
# classifier = pickle.load(f)
# f.close()
# classifier = nltk.data.load("my_classifier.pickle")
genre_accuracy = []
for key, value in sorted(self.sentim_analyzer.evaluate(test_set).items()):
# print('{0}: {1}'.format(key, value))
if key == 'Precision [action]':
genre_accuracy.append(('action', value))
if key == 'Precision [comedy]':
genre_accuracy.append(('comedy', value))
if key == 'Precision [drama]':
genre_accuracy.append(('drama', value))
if key == 'Precision [crime]':
genre_accuracy.append(('crime', value))
return genre_accuracy
# Takes in a query
# Outputs a dictionary of movie indices movies to weights where weight is to be added to all quote scores of movies
def find_ml(self, td):
f_tokenizer = TreebankWordTokenizer()
query_words = f_tokenizer.tokenize(td)
genres = self.sentiment_analysis(query_words)
weighted_genres = []
genre_weights = {}
for x in genres:
if x[1] is not None:
weighted_genres.append(x[0])
genre_weights[x[0]] = x[1]
d_score_updates = {}
for movie in self.movies:
g = self.genre_dict[movie][0]
total_genre_score = 0
if u'Comedy' in g and 'comedy' in weighted_genres:
total_genre_score += genre_weights['comedy']
if u'Action' in g and 'action' in weighted_genres:
total_genre_score += genre_weights['action']
if u'Crime' in g and 'crime' in weighted_genres:
total_genre_score += genre_weights['crime']
if u'Drama' in g and 'drana' in weighted_genres:
total_genre_score += genre_weights['drama']
d_score_updates[self.movies.index(movie)] = total_genre_score * .1
return d_score_updates
