input: 
  from typing import List
  from nltk.classify import NaiveBayesClassifier
  import nltk
  
  def classify_text(text: List[str]) -> str:
      """
      Given a list of text strings, classify each text into one of two classes: 'positive' or 'negative'.
      The function uses a NaiveBayesClassifier trained on predefined positive and negative text examples.
      >>> classify_text(["This is a great movie!", "This movie was terrible"])
      ['positive', 'negative']
      """
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
  
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positive_tokens = nltk.word_tokenize(positiveText) 
      negative_tokens = nltk.word_tokenize(negativeText)
  
      positive_data = [(label_words(positive_tokens), 'positive')] 
      negative_data = [(label_words(negative_tokens), 'negative')]
   
      train_data = positive_data + negative_data 
      classifier = NaiveBayesClassifier.train(train_data)
  
      results = [classifier.classify(label_words(nltk.word_tokenize(t))) for t in text]
      return results
  
output:     return results