def emoticons_from_dictionary(text,dict_emoticons):
feature_set = dict()
feature_set['score_emoticons']=0
feature_set['number_emoticons']=0
tweet_tokens_emoticons=clean_text.normalize_emoticons(text)
text = tweet_tokens_emoticons.split()
for emoticon in text:
if emoticon in dict_emoticons:
feature_set['score_emoticons']+=dict_emoticons[emoticon]
feature_set['score_'+emoticon]=dict_emoticons[emoticon]
feature_set['number_emoticons']+=1
if tweet_tokens_emoticons.endswith(emoticon):
if dict_emoticons[emoticon] > 0:
feature_set['ends_with_positive_emoticon']=True
elif dict_emoticons[emoticon] <0:
feature_set['end_with_negative_emoticon']=True
else:
feature_set['end_with_neutral_emoticon']=True
return feature_set
def emoticons_from_dictionary(text,dict_emoticons):
"""This function returns the number of emoticons, the score that each one has, the total score and if the sentence is
finished with a negative, positive or neutral emoticon
"""
feature_set = dict()
feature_set['score_emoticons']=0
feature_set['number_emoticons']=0
tweet_tokens_emoticons=clean_text.normalize_emoticons(text)
text = tweet_tokens_emoticons.split()
for emoticon in text:
if emoticon in dict_emoticons:
feature_set['score_emoticons']+=dict_emoticons[emoticon]
feature_set['score_'+emoticon]=dict_emoticons[emoticon]
feature_set['number_emoticons']+=1
if tweet_tokens_emoticons.endswith(emoticon):
if dict_emoticons[emoticon] > 0:
feature_set['ends_with_positive_emoticon']=True
elif dict_emoticons[emoticon] <0:
feature_set['end_with_negative_emoticon']=True
else:
feature_set['end_with_neutral_emoticon']=True
return feature_set
def normalize_emoticons_test(self):
texts=[":))",":((","::)",":)))))"]
texts_clean=[":)",":(",":)",":)"]
for i in range(len(texts)):
self.assertEqual(ct.normalize_emoticons(texts[i]),texts_clean[i])