'default': 'marl:Neutral' } 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 test_cases = [{ 'input': 'Hello :)', 'polarity': 'marl:Positive' }, { 'input': 'So sad :(', 'polarity': 'marl:Negative' }, { 'input': 'Yay! Emojis 😁', 'polarity': 'marl:Positive' }, { 'input': 'But no emoticons 😢', 'polarity': 'marl:Negative' }] if __name__ == '__main__': easy_test()
'''Provides sentiment annotation using a lexicon''' author = '@balkian' version = '0.1' 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] test_cases = [{ 'input': u'Hello :)', 'polarity': 'marl:Positive' }, { 'input': u'So sad :(', 'polarity': 'marl:Negative' }, { 'input': u'Yay! Emojis 😁', 'polarity': 'marl:Positive' }, { 'input': u'But no emoticons 😢', 'polarity': 'marl:Negative' }] if __name__ == '__main__': easy_test()