'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()
