class_docstr: Naive Bayes analyzer that is trained on a dataset of movie reviews.
  Returns results as a named tuple of the form:
  ``Sentiment(classification, p_pos, p_neg)``
  
  :param callable feature_extractor: Function that returns a dictionary of
      features, given a list of words.
docstr: Return the sentiment as a named tuple of the form:
  ``Sentiment(classification, p_pos, p_neg)``
docstr_tok: ["Return","the","sentiment","as","a","named","tuple","of","the","form",":","``","Sentiment","(","classification",",","p_pos",",","p_neg",")","``"]
code: def analyze(self, text):
      """Return the sentiment as a named tuple of the form:
      ``Sentiment(classification, p_pos, p_neg)``
      """
      # Lazily train the classifier
      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"),
      )
  
file_path: TextBlob/textblob/en/sentiments.py
imports: from __future__ import absolute_import
  from collections import namedtuple
  import nltk
  from textblob.en import sentiment
  from textblob.tokenizers import word_tokenize
  from textblob.decorators import requires_nltk_corpus
  from textblob.base import BaseSentimentAnalyzer, DISCRETE, CONTINUOUS
label_desc: Use image node_id 3 for calling the NaiveBayesAnalyzer obj's underlying member method code with example usage: obj.analyze(text) and returns: self