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: Train the Naive Bayes classifier on the movie review corpus.
docstr_tok: ["Train","the","Naive","Bayes","classifier","on","the","movie","review","corpus","."]
code: def train(self):
      """Train the Naive Bayes classifier on the movie review corpus."""
      super(NaiveBayesAnalyzer, self).train()
      neg_ids = nltk.corpus.movie_reviews.fileids("neg")
      pos_ids = nltk.corpus.movie_reviews.fileids("pos")
      neg_feats = [
          (
              self.feature_extractor(
                  nltk.corpus.movie_reviews.words(fileids=[f])
              ),
              "neg",
          )
          for f in neg_ids
      ]
      pos_feats = [
          (
              self.feature_extractor(
                  nltk.corpus.movie_reviews.words(fileids=[f])
              ),
              "pos",
          )
          for f in pos_ids
      ]
      train_data = neg_feats + pos_feats
      self._classifier = nltk.classify.NaiveBayesClassifier.train(
          train_data
      )
  
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 2 for calling the NaiveBayesAnalyzer obj's underlying member method code with example usage: obj.train() without return types