def getSentics(self, word):
        senticsAndItensity = []
        sn = SenticNet('en')
        try:
            sentics = sn.sentics(word)
            polarity_intensity = sn.polarity_intense(word)
            # print(sentics)
            # print(sentics['pleasantness'])
            # print(sentics['attention'])
            # print(sentics['sensitivity'])
            # print(sentics['aptitude'])
            # print(polarity_intensity)

            senticsAndItensity.append(float(sentics['pleasantness']))
            senticsAndItensity.append(float(sentics['attention']))
            senticsAndItensity.append(float(sentics['sensitivity']))
            senticsAndItensity.append(float(sentics['aptitude']))
            senticsAndItensity.append(float(polarity_intensity))

            return senticsAndItensity

        except Exception as e:
            defaultsentics = [0.0, 0.0, 0.0, 0.0, 0.0]
            return defaultsentics


# ##TESTING AREA
# yas = SenticValuer()
# print(yas.getSentics("awkward"))
Beispiel #2
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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()]))
Beispiel #3
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from senticnet.senticnet import SenticNet

sn = SenticNet('ru')

word = input('Введите ваш комментарий(например "как дела"): ')

lst = word.split()

#concept_info = sn.concept(word)
#polarity_value = sn.polarity_value(word)
#polarity_intense = sn.polarity_intense(word)
#moodtags = sn.moodtags(word)
#semantics = sn.semantics(word)

print(list(map(lambda x: sn.sentics(x), lst)))
pop = input(" ")
Beispiel #4
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# Each line of corpus must be equivalent to each document of the corpus
#boc_model=boc.BOCModel(doc_path="input corpus path")
boc_model = boc.BOCModel('text.txt')

#boc_model.context = text

# output can be saved with save_path parameter
boc_matrix, word2concept_list, idx2word_converter = boc_model.fit()

# SenitcNet lexicon lookup
from senticnet.senticnet import SenticNet

sn = SenticNet()

concept_info = sn.concept(text)
polarity_value = sn.polarity_value(text)
polarity_intense = sn.polarity_intense(text)
moodtags = sn.moodtags(text)
semantics = sn.semantics(text)
sentics = sn.sentics(text)

print('==================================')
print('test: ', text)
print('concept_info: ', concept_info)
print('polarity_value: ', polarity_value)
print('polarity_intense: ', polarity_intense)
print('moodtags: ', moodtags)
print('semantics: ', semantics)
print('sentics: ', sentics)
print('==================================')
from senticnet.senticnet import SenticNet

teste = []
sn = SenticNet('pt')
concept_info = sn.concept('amor')
polarity_value = sn.polarity_value('amor')
polarity_intense = sn.polarity_intense('amor')
moodtags = sn.moodtags('amor')
semantics = sn.semantics('amor')
sentics = sn.sentics('amor')

teste.append(concept_info)

print(teste)