def test_balance(self):
"""Balanced use of negative and positive words -> neutral"""
twfeat = extract_features(normalize_text("friend and enemy"))
prob_result = self.classifier.prob_classify(twfeat)
score = prob_result.prob('positive') - prob_result.prob('negative')
self.assertEqual(score, 0)
def test_garbage(self):
"""Guessing garbage should is neutral"""
twfeat = extract_features(normalize_text("goregho regeorg egewg"))
prob_result = self.classifier.prob_classify(twfeat)
score = prob_result.prob('positive') - prob_result.prob('negative')
self.assertEqual(score, 0.0)
def test_polarized(self):
"""Guessing polarized words shouldn't fail"""
twfeat = extract_features(normalize_text("This car is my best friend "
"and enemy."))
prob_result = self.classifier.prob_classify(twfeat)
score = prob_result.prob('positive') - prob_result.prob('negative')
self.assertTrue(score > 0, "Score is {}".format(score))
def setUp(self):
pos_tweets = [('I love this car', 'positive'),
('This view is amazing', 'positive'),
('I feel great this morning', 'positive'),
('I am so excited about the concert', 'positive'),
('He is my best friend', 'positive')]
neg_tweets = [('I do not like this car', 'negative'),
('This view is horrible', 'negative'),
('I feel tired this morning', 'negative'),
('I am not looking forward to the concert', 'negative'),
('He is my enemy', 'negative')]
tweets = []
for (words, sentiment) in pos_tweets + neg_tweets:
tweets.append((normalize_text(words), sentiment))
training_set = [(extract_features(doc), label) for (doc, label)
in tweets]
self.classifier = NaiveBayesClassifier.train(training_set)
def _guess(self, message):
twfeat = extract_features(normalize_text(message))
result = self.classifier.prob_classify(twfeat)
return format(result.prob('positive') - result.prob('negative'))
def _extract_documents(tweets, label):
return [(normalize_text(tweet), label) for tweet in tweets]
def _extract_documents(tweets, label):
return [(normalize_text(tweet), label) for tweet in tweets]
