class Featurizer(object):
def __init__(self):
self.sentiment_analyzer = Sentiment('data/AFINN-111.txt')
self.bow_vectorizer = None
self.bow_analyzer = None
def bag_of_words(self, body):
return self.bow_vectorizer.transform([body]).toarray()
def text_features(self, comment):
num_chars = len(comment.get("body"))
num_links = count_links(comment.get("body"))
simple_tokens = comment.get("body").split(' ')
num_words = 0
avg_word_length = 0
for token in simple_tokens:
num_words += 1
avg_word_length += len(token)
avg_word_length = float(avg_word_length) / float(num_words)
sentiment = self.sentiment_analyzer.analyze(
self.bow_analyzer(comment.get("body")))
score = comment.get("score")
return [num_chars, num_links, num_words, num_words,
avg_word_length, sentiment]
def transform_comment(self, comment):
return numpy.hstack((
numpy.array([self.text_features(comment)],
dtype='float_'),
self.bag_of_words(comment.get("body"))))
def score_comment(self, comment):
return comment.get("score")
def transform(self, comments):
""" Returns a Nx(D+1) numpy matrix of features. The first D columns
correspond to features, where the final column corresponds to the
scores of each comment"""
# if it's a single instance, return an array
if isinstance(comments, dict):
return transform_comment(comments)
# http://scikit-learn.org/stable/modules/feature_extraction.html
self.bow_vectorizer = CountVectorizer(min_df=1)
self.bow_vectorizer.fit([c.get("body") for c in comments])
self.bow_analyzer = self.bow_vectorizer.build_analyzer()
def features_and_label(comment):
return numpy.hstack((
self.transform_comment(comment),
numpy.array([[self.score_comment(comment)]],
dtype='float_')))
return numpy.vstack([features_and_label(c)
for c in comments])
class Featurizer(object):
def __init__(self):
self.sentiment_analyzer = Sentiment('data/AFINN-111.txt')
self.bow_vectorizer = None
self.bow_analyzer = None
def bag_of_words(self, body):
return self.bow_vectorizer.transform([body]).toarray()
def text_features(self, comment):
num_chars = len(comment.get("body"))
num_links = count_links(comment.get("body"))
simple_tokens = comment.get("body").split(' ')
num_words = 0
avg_word_length = 0
for token in simple_tokens:
num_words += 1
avg_word_length += len(token)
avg_word_length = float(avg_word_length) / float(num_words)
sentiment = self.sentiment_analyzer.analyze(
self.bow_analyzer(comment.get("body")))
score = comment.get("score")
return [
num_chars, num_links, num_words, num_words, avg_word_length,
sentiment
]
def transform_comment(self, comment):
return numpy.hstack((numpy.array([self.text_features(comment)],
dtype='float_'),
self.bag_of_words(comment.get("body"))))
def score_comment(self, comment):
return comment.get("score")
def transform(self, comments):
""" Returns a Nx(D+1) numpy matrix of features. The first D columns
correspond to features, where the final column corresponds to the
scores of each comment"""
# if it's a single instance, return an array
if isinstance(comments, dict):
return transform_comment(comments)
# http://scikit-learn.org/stable/modules/feature_extraction.html
self.bow_vectorizer = CountVectorizer(min_df=1)
self.bow_vectorizer.fit([c.get("body") for c in comments])
self.bow_analyzer = self.bow_vectorizer.build_analyzer()
def features_and_label(comment):
return numpy.hstack((self.transform_comment(comment),
numpy.array([[self.score_comment(comment)]],
dtype='float_')))
return numpy.vstack([features_and_label(c) for c in comments])
def GetSubjScoreForSingleSent(SentIndex, Sentence):
T0 = time()
try:
SentAnalyzer = Sentiment()
ResDict = SentAnalyzer.analyze([Sentence])
pprint(ResDict)
SentSentimentPol = {S: {'sentiment':ResDict['sentiments'][I], 'score': ResDict['scores'][I]} for I,S in enumerate(ResDict['sentences'])}
print 'processed sentence number {} in {} sec'.format(SentIndex, round(time() - T0))
return SentSentimentPol
except:
print 'failed to process sentence number ', SentIndex
return {}
def ProcessSingleFile (FName, MailNumber):
T0 = time()
try:
EmailText = [open(FName).read()]
SentAnalyzer = Sentiment()
Res = SentAnalyzer.analyze(EmailText)
pprint (Res)
SentSentimentPol = zip(Res['sentences'],Res['sentiments'],Res['scores'])
SentSentimentPol = [ThreeTuple for ThreeTuple in SentSentimentPol if ThreeTuple[1] != 'neutral']
print 'processed email {} in {} sec'.format(MailNumber, round(time()-T0))
return SentSentimentPol
except:
print 'failed to process email ', MailNumber
return []
