input: def analyze_sentiment(text):
      """
      Analyze the sentiment of given text and categorize it into positive, negative, and neutral sentiments.
      The sentiment is determined by counting the number of sentences classified as positive and negative using a NaiveBayesClassifier.
      If the count of negative sentences is greater than the count of positive sentences, the overall sentiment is negative.
      If the count of positive sentences is greater than the count of negative sentences, the overall sentiment is positive.
      If the counts are equal, the overall sentiment is neutral.
      >>> analyze_sentiment("Today was a good day. Everything went well.")
      'positive'
      >>> analyze_sentiment("I am not feeling happy. This day was terrible.")
      'negative'
      >>> analyze_sentiment("It is an ordinary day with no special events.")
      'neutral'
      """
output:     from textblob import TextBlob
  
      blob = TextBlob(text)
      positive_count = 0
      negative_count = 0
  
      for sentence in blob.sentences:
          sentiment = sentence.sentiment.polarity
          if sentiment > 0:
              positive_count += 1
          elif sentiment < 0:
              negative_count += 1
  
      if negative_count > positive_count:
          return 'negative'
      elif positive_count > negative_count:
          return 'positive'
      else:
          return 'neutral'