input: Write a function to determine if a given sentence is an 'affirmative' sentence or a 'negative' sentence. The function should use natural language processing to identify negation words and classify the sentence accordingly. If the sentence contains any negation words, it should be classified as 'Negative'; otherwise, it should be classified as 'Affirmative'.
  
  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
  
  def isAffirmativeorNegative(sentence):
      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 is always on time.") == "Affirmative"
  assert isAffirmativeorNegative("There is no problem.") == "Negative"
  assert isAffirmativeorNegative("Everybody loves this movie.") == "Affirmative"
  assert isAffirmativeorNegative("I cannot come today.") == "Negative"
output: def isAffirmativeorNegative(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)
      for word in words:
          if word in negationwords:
              return "Negative"
      return "Affirmative"