input: from typing import List
  
  def analyze_sentiment(text: str) -> str:
      """Analyzes the sentiment of given text and categorizes it into positive, negative, and neutral sentiments.
      Returns 'positive' if more sentences are positive, 'negative' if more sentences are negative, and 'neutral' otherwise.
      >>> analyze_sentiment("I had a wonderful day. Everything was perfect.")
      'positive'
      >>> analyze_sentiment("It was a disappointing experience. Nothing went right.")
      'negative'
      >>> analyze_sentiment("Today's weather was just fine.")
      '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
output:     from nltk.tokenize import sent_tokenize
      from nltk.classify import NaiveBayesClassifier