input: Write a function to classify the sentiment of movie reviews using Natural Language Processing and the Vader Sentiment library. The function should return 'The sentiment of the sentence is negative!' if the negative score is greater than the positive score, otherwise it should return 'The sentiment of the sentence is positive!'. If the input sentence is empty, it should return 'No input provided'.
  import nltk
  from nltk.sentiment import vader
  def classify_sentiment(sentence):
    if not sentence:
      return 'No input provided'
    sid = vader.SentimentIntensityAnalyzer()
    scores = sid.polarity_scores(sentence)
    if scores['neg'] > scores['pos']:
      return 'The sentiment of the sentence is negative!'
    else:
      return 'The sentiment of the sentence is positive!'
  assert classify_sentiment('The movie was terrible. It felt like it would never end!')=='The sentiment of the sentence is negative!'
  assert classify_sentiment('I loved the movie. The acting and the plot were fantastic!')=='The sentiment of the sentence is positive!'
  assert classify_sentiment('')=='No input provided'
output: def classify_sentiment(sentence):
    if not sentence:
      return 'No input provided'
    sid = vader.SentimentIntensityAnalyzer()
    scores = sid.polarity_scores(sentence)
    if scores['neg'] > scores['pos']:
      return 'The sentiment of the sentence is negative!'
    else:
      return 'The sentiment of the sentence is positive!'