def _get_classifier(self):
from twentiment.naivebayes import NaiveBayesClassifier
# from nltk.classify.naivebayes import NaiveBayesClassifier
training_features = [
({'nice': True, 'pretty': True}, 'pos'),
({'ugly': True, 'bald': True}, 'neg')
]
return NaiveBayesClassifier.train(training_features)
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 from_training_set(cls, training_set):
"""Creates a new instance from the given training set."""
classifier = NaiveBayesClassifier.train(training_set)
return cls(classifier)
def from_training_set(cls, training_set):
"""Creates a new instance from the given training set."""
classifier = NaiveBayesClassifier.train(training_set)
return cls(classifier)
