input: Write a function to determine if a given sentence is classified as 'Affirmative' or 'Negative' by detecting the presence of negation words in the sentence. If the sentence contains any negation words, it should be classified as 'Negative'; otherwise, it should be classified as 'Affirmative'. You can use the NLTK library to tokenize the sentence and find negation words.
  assert isAffirmativeorNegative("He always comes late but I dont mind.") == "Negative"
  assert isAffirmativeorNegative("She is always on time.") == "Affirmative"
  assert isAffirmativeorNegative("I have no idea what you are talking about.") == "Negative"
  assert isAffirmativeorNegative("Everyone understands the instructions.") == "Affirmative"
  assert isAffirmativeorNegative("He is not ready yet.") == "Negative"
output: def isAffirmativeorNegative(sentence):
      from nltk.tokenize import word_tokenize
      negation_words = {"not", "no", "never", "none", "nobody", "nothing", "neither", "nowhere", "hardly", "scarcely", "barely", "don't", "doesn't", "didn't", "can't", "won't", "isn't", "aren't", "wasn't", "weren't"}
      tokens = word_tokenize(sentence.lower())
      for word in tokens:
          if word in negation_words:
              return "Negative"
      return "Affirmative"