input: Write a function to classify text documents into two classes: positive and negative using NaiveBayesClassifier from the nltk library. The function should return the classification result ('positive' or 'negative') for a given text input. If the given text input is empty then return an empty string.
  assert classify_text_sentiment("This is a great movie!")=='positive'
  assert classify_text_sentiment("This movie was terrible")=='negative'
  assert classify_text_sentiment("This movie was ok")=='positive'
  assert classify_text_sentiment("")==''
output: def classify_text_sentiment(text):
      if not text:
          return ''
      from nltk.classify import NaiveBayesClassifier
      from nltk.corpus import subjectivity
      from nltk.sentiment import SentimentAnalyzer
      from nltk.sentiment.util import extract_unigram_feats
      from nltk.sentiment.util import mark_negation
      from nltk.corpus import movie_reviews
      from nltk.tokenize import word_tokenize
      
      documents = [(list(movie_reviews.words(fileid)), category)
                   for category in movie_reviews.categories()
                   for fileid in movie_reviews.fileids(category)]
      
      all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())
      word_features = list(all_words)[:2000]
      
      def document_features(document):
          document_words = set(document)
          features = {}
          for word in word_features:
              features['contains({})'.format(word)] = (word in document_words)
          return features
      
      featuresets = [(document_features(d), c) for (d,c) in documents]
      train_set, test_set = featuresets[100:], featuresets[:100]
      classifier = NaiveBayesClassifier.train(train_set)
      
      words = word_tokenize(text)
      features = document_features(words)
      result = classifier.classify(features)
      return result