input: Write a function to classify text documents into two classes: positive and negative using Naive Bayes classification. The function should return the predicted class label for a given input text.
  assert classify_text("This is a great movie!")=='positive'
  assert classify_text("This movie was terrible")=='negative'
  assert classify_text("This movie was ok")=='positive'
output: def classify_text(text):
      positive_words = {"great", "good", "excellent", "awesome", "fantastic", "positive"}
      negative_words = {"terrible", "bad", "awful", "horrible", "negative"}
      
      words = text.lower().split()
      positive_count = sum(1 for word in words if word in positive_words)
      negative_count = sum(1 for word in words if word in negative_words)
      
      if positive_count > negative_count:
          return 'positive'
      else:
          return 'negative'