input: Write a python function to classify a text review as "Positive", "Negative", or "Neutral". Use nltk to tokenize the text and remove stopwords, then classify based on the proportion of positive words. 
  assert classify_review("I love this product, it is amazing!")=="Positive"
  assert classify_review("This is a terrible product and I hate it.")=="Negative"
  assert classify_review("It is an average product with nothing special.")=="Neutral"
output: def classify_review(text):
      import nltk
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
  
      nltk.download('vader_lexicon')
      nltk.download('stopwords')
      nltk.download('punkt')
  
      stop_words = set(stopwords.words('english'))
      word_tokens = word_tokenize(text)
  
      filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]
  
      sid = SentimentIntensityAnalyzer()
      sentiment_scores = sid.polarity_scores(" ".join(filtered_sentence))
  
      if sentiment_scores['compound'] >= 0.05:
          return "Positive"
      elif sentiment_scores['compound'] <= -0.05:
          return "Negative"
      else:
          return "Neutral"