input: Write a function to determine if a given sentence is an 'affirmative' sentence or a 'negative' sentence. If the sentence contains negation words, it should be classified as 'Negative'; otherwise, it should be classified as 'Affirmative'. The function should use the NLTK library to tokenize the sentence and identify negation words. If the sentence is empty, the function should return "Affirmative".
  
  def isAffirmativeorNegative(sentence):
      import nltk
      nltk.download('stopwords')
      from nltk.corpus import stopwords 
      
      # Function to find the negation words
      def findNegation(sentence):
          negationwords = {'not', 'never', 'no', 'none', 'non', 'neither',
                           'nobody', 'nowhere', 'cannot', 'couldnt', 'shouldnt', 
                           'wouldnt', 'dont', 'didnt', 'hasnt', 'havent', 'isnt', 'arent'} 
          words = nltk.word_tokenize(sentence)
          neg = []
          for word in words:
              if word in negationwords:
                  neg.append(word)
          return neg
      
      if not sentence:
          return "Affirmative"
      
      negationwords = findNegation(sentence)
      if len(negationwords) == 0: 
          return "Affirmative" 
      else: 
          return "Negative"
  
  assert isAffirmativeorNegative("He always comes late but I dont mind.") == "Negative"
  assert isAffirmativeorNegative("She likes ice cream.") == "Affirmative"
  assert isAffirmativeorNegative("No one could help.") == "Negative"
  assert isAffirmativeorNegative("") == "Affirmative"
  assert isAffirmativeorNegative("I will never give up.") == "Negative"
output: def isAffirmativeorNegative(sentence):
      import nltk
      nltk.download('punkt')
      
      negationwords = {'not', 'never', 'no', 'none', 'non', 'neither',
                       'nobody', 'nowhere', 'cannot', 'couldnt', 'shouldnt', 
                       'wouldnt', 'dont', 'didnt', 'hasnt', 'havent', 'isnt', 'arent'} 
      
      if not sentence:
          return "Affirmative"
      
      words = nltk.word_tokenize(sentence)
      for word in words:
          if word in negationwords:
              return "Negative"
      
      return "Affirmative"