input: Write a function to design a machine learning algorithm that can identify whether a given sentence is an 'affirmative' sentence or a 'negative' sentence. The function should use NLTK to tokenize the sentence and identify if any negation words are present. If no negation words are found, the sentence is considered 'affirmative'; otherwise, it is considered 'negative'. 
  
  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("Everything is going well.")=="Affirmative"
  assert isAffirmativeorNegative("There is no problem here.")=="Negative"
  assert isAffirmativeorNegative("Everybody is happy.")=="Affirmative"
  assert isAffirmativeorNegative("I cannot go to the party.")=="Negative"
output: 
  def isAffirmativeorNegative(sentence):
      import nltk
      from nltk.tokenize import word_tokenize
      negationwords = ["not", "no", "never", "none", "nobody", "nothing", "neither", "nowhere", "hardly", "scarcely", "barely", "doesn't", "isn't", "wasn't", "shouldn't", "wouldn't", "couldn't", "won't", "can't", "don't"]
      tokens = word_tokenize(sentence.lower())
      negationwords = [word for word in tokens if word in negationwords]
      if len(negationwords) == 0:
          return "Affirmative"
      else:
          return "Negative"
  