def predict_one(self, features, **kwargs): output = basic.get_polarity(features[0]) if output == 'pos': return [1, 0, 0] if output == 'neu': return [0, 1, 0] return [0, 0, 1]
def predict_one(self, features, **kwargs): output = basic.get_polarity(features[0]) if output == 'pos': return [1, 0, 0] if output == 'neg': return [0, 0, 1] return [0, 1, 0]
def analyse_entry(self, entry, activity): polarity = basic.get_polarity(entry.text) polarity = self.mappings.get(polarity, self.mappings['default']) s = models.Sentiment(marl__hasPolarity=polarity) s.prov(activity) entry.sentiments.append(s) yield entry
def analyse_entry(self, entry, *args, **kwargs): polarity = basic.get_polarity(entry.text, self.dictionaries) if polarity in self.mappings: polarity = self.mappings[polarity] s = models.Sentiment(marl__hasPolarity=polarity) s.prov(self) entry.sentiments.append(s) yield entry
def analyse_entry(self, entry, params): positive_words = params['positive-words'].split(',') negative_words = params['negative-words'].split(',') dictionary = { 'marl:Positive': positive_words, 'marl:Negative': negative_words, } polarity = basic.get_polarity(entry.text, [dictionary]) s = models.Sentiment(marl__hasPolarity=polarity) s.prov(self) entry.sentiments.append(s) yield entry
def analyse_entry(self, entry, activity): params = activity.params positive_words = params['positive-words'].split(',') negative_words = params['negative-words'].split(',') dictionary = { 'marl:Positive': positive_words, 'marl:Negative': negative_words, } polarity = basic.get_polarity(entry.text, [dictionary]) s = models.Sentiment(marl__hasPolarity=polarity) s.prov(activity) entry.sentiments.append(s) yield entry
def predict_one(self, input): return basic.get_polarity(input)
def predict_one(self, input): output = basic.get_polarity(input) return self.mappings.get(output, self.mappings['default'])