def get_emotions(tokens): from senticnet.senticnet import SenticNet result = {} sn = SenticNet() for token in tokens: moodtags = "" if token in sn.data: moodtags = sn.moodtags(token) print(token, moodtags) #TODO return result
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()]))
# 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)