input: 
  from typing import List
  
  def analyze_sentiment(text: str) -> str:
      """ Analyzes the sentiment of the given text by categorizing it into 'positive', 'negative', or 'neutral'.
      It tokenizes the text into sentences, uses a NaiveBayesClassifier to determine the sentiment of each sentence,
      and then decides the overall sentiment based on the counts of positive and negative sentiments.
      >>> analyze_sentiment('I love this product.')
      'positive'
      >>> analyze_sentiment('This is not a good experience.')
      'negative'
      >>> analyze_sentiment('This is just a statement.')
      'neutral'
      """
      sents = sent_tokenize(text)
      neg = 0
      pos = 0
      for sentence in sents:
          sentiment_analysis = NaiveBayesClassifier.classify(sentence)
          if sentiment_analysis == 'neg':
              neg += 1
          if sentiment_analysis == 'pos':
              pos += 1
      
      if neg > pos:
          sentiment = 'negative'
      elif pos > neg:
          sentiment = 'positive'
      else:
          sentiment = 'neutral'
      
      return sentiment
  
  # End analyze sentiment definition
  
output:     from nltk.tokenize import sent_tokenize
      from nltk.classify import NaiveBayesClassifier
  
      # Assuming NaiveBayesClassifier is trained and available
      # Example training code for NaiveBayesClassifier:
      # training_data = [("I love this product.", 'pos'), ("This is not a good experience.", 'neg')]
      # NaiveBayesClassifier.train(training_data)