def from_json(cls, json, max_entries=0):
"""Creates a new instance from the given JSON data as dict data
structure.
:param max_entries: Limit training set to a maximum of ``max_entries``
items. This can be helpful to reduce memory usage. A value of 0 or
less means no limit.
"""
pos_tweets, neg_tweets = _limited_tweet_split(json, max_entries)
tweets = _extract_documents(pos_tweets, "positive") + _extract_documents(neg_tweets, "negative")
training_set = [(extract_features(doc), label) for (doc, label) in tweets]
return cls.from_training_set(training_set)
def from_json(cls, json, max_entries=0):
"""Creates a new instance from the given JSON data as dict data
structure.
:param max_entries: Limit training set to a maximum of ``max_entries``
items. This can be helpful to reduce memory usage. A value of 0 or
less means no limit.
"""
pos_tweets, neg_tweets = _limited_tweet_split(json, max_entries)
tweets = (_extract_documents(pos_tweets, 'positive') +
_extract_documents(neg_tweets, 'negative'))
training_set = [(extract_features(doc), label)
for (doc, label) in tweets]
return cls.from_training_set(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'))
