def getInfo(self, concept):
try:
sn = Senticnet()
concept_info = sn.concept(concept)
polarity_value = sn.polarity_value(concept)
polarity_intense = sn.polarity_intense(concept)
moodtags = sn.moodtags(concept)
semantics = sn.semantics(concept)
sentics = sn.sentics(concept)
"""print(concept)
print("concept_info: {}".format(concept_info))
print("polarity_value: {}".format(polarity_value))
print("polarity_intense: {}".format(polarity_intense))
print("moodtags: {}".format(moodtags))
print("semantics: {}".format(semantics))
print("sentics: {}".format(sentics))
print("\n\n")"""
return "{} - {}".format(polarity_value, polarity_intense)
except:
return "NOT POSSIBLE"
from senticnet.senticnet import Senticnet
sn = Senticnet()
print "polarity value:", sn.polarity_value("love")
print "polarity intense:", sn.polarity_intense("love")
print "moodtags:", ", ".join(sn.moodtags("love"))
print "semantics:", ", ".join(sn.semantics("love"))
print "\n".join(
[key + ": " + str(value) for key, value in sn.sentics("love").items()])
for concept in concepts:
concept = concept.replace('[', '')
concept = concept.replace('u\'', '')
concept = concept.replace('_', ' ')
concept = concept.replace('...', '')
concept = concept.replace(']', '')
concept = concept.replace('\'', '')
concept = concept.split(',')
print('internal loop')
for item in concept:
print('item:')
print(item)
item = str(item)
#item='love'
try:
concept_info = sn.concept(item)
polarity_value = sn.polarity_value(item)
polarity_intense = sn.polarity_intense(item)
print(polarity_value)
moodtags = sn.moodtags(item)
semantics = sn.semantics(item)
sentics = sn.sentics(item)
polarity_intense_result.append(polarity_intense)
polarity_value_result.append(polarity_value)
concept_info_result.append(concept_info)
except:
print('Concept not in senticnet')
print(polarity_value_result)
train_set = negative_features + positive_features
classifier = NaiveBayesClassifier.train(train_set)
# Основная работа
sentence = 'Тест'
sentence = sentence.lower()
words = sentence.split(' ')
neg = 0
pos = 0
mood = 0
for word in words:
word = morph.parse(word.decode('utf8'))[0].normal_form
try: # пробуем получить теги из SenticNet
moodtags = sn.moodtags(word.encode('utf8'))
for item in moodtags:
mood += 1
if item in negative_vocab:
neg += 1
elif item in positive_vocab:
pos += 1
except: # проверяем, не входит ли слово в словарь
if word in negative_vocab:
neg += 1
mood += 1
elif word in positive_vocab:
pos += 1
mood += 1
# если ничего не помогает, пытаемся классифицировать слово через наивный Баесовский классификатор
