'Return the sentiment as a named tuple of the form:
``Sentiment(classification, p_pos, p_neg)``'

def analyze(self, text):

    super(NaiveBayesAnalyzer, self).analyze(text)
    tokens = word_tokenize(text, include_punc=False)
    filtered = (t.lower() for t in tokens if (len(t) >= 3))
    feats = self.feature_extractor(filtered)
    prob_dist = self._classifier.prob_classify(feats)
    return self.RETURN_TYPE(classification=prob_dist.max(), p_pos=prob_dist.prob('pos'), p_neg=prob_dist.prob('neg'))
