def __call__(self):
previous_msg = models.Message.fetch_previous_msg(self.user.id)
w_toks = WordFormatter.stoks2wtoks([previous_msg])
message_normalizer = MessageNormalizer()
df = message_normalizer(w_toks, self.user.sender_id, from_preprocessor=False)
text_kw_df_gengerator = TextKwDFGenerator()
text_kw_df = text_kw_df_gengerator(df)
sentiment_score_df_generator = SentimentScoreDFGenerator()
sentiment_score_df = sentiment_score_df_generator(df, text_kw_df)
responses = self.create_cmp(df, sentiment_score_df, self.response_data)
self.response_data['regular'] = responses
return self.response_data
def __is_previous_msg_cmp_makeable(cls, user_id):
previous_msg = models.Message.fetch_previous_msg(user_id)
if len(previous_msg) == 0:
return False
w_toks = WordFormatter.stoks2wtoks([previous_msg])
message_normalizer = MessageNormalizer()
df = message_normalizer(w_toks, None, from_preprocessor=False)
target_pos = Nlp_util.pos_VERBs + Nlp_util.pos_ADVERBs + Nlp_util.pos_ADJECTIVEs
target_word_df = df[df.pos.isin(target_pos)]
if any(target_word_df.base_form.isin(WORD_LIST_FOR_CMP.word.tolist())):
return True
else:
return False
