input: Write a python function to classify words based on sentiment scores.
  assert classify_words("The conference was an incredible success. All the participants were thrilled and delighted with the outcome.", [0.8, 0.7, 0.7, 0.5, 0.6, 0.9, 0.2, 0.3, 0.8, 0.9, 0.9, 0.6]) == (['conference', 'incredible', 'success', 'participants', 'delighted', 'outcome'], ['The', 'was', 'an', 'All', 'were', 'with', 'the'], ['thrilled'])
output: def classify_words(sentence, scores):
      words = sentence.split()
      positive_words = []
      neutral_words = []
      negative_words = []
  
      for word, score in zip(words, scores):
          if score > 0.6:
              positive_words.append(word)
          elif score >= 0.4:
              neutral_words.append(word)
          else:
              negative_words.append(word)
  
      return (positive_words, neutral_words, negative_words)