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.
code: def __init__(self, feature_extractor=_default_feature_extractor):
      super(NaiveBayesAnalyzer, self).__init__()
      self._classifier = None
      self.feature_extractor = feature_extractor
  
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 1 to create a new NaiveBayesAnalyzer object from inherited base classes: BaseSentimentAnalyzer with example: obj = NaiveBayesAnalyzer(feature_extractor)