input: Write a function to create a sentiment analyzer for a given text. The function tokenizes the text, applies stemming, removes punctuation and stopwords, and finally uses the SentimentIntensityAnalyzer to get the polarity score of the text.
  
  def sentiment_analyzer(text):
      # Tokenize the text
      tokens = word_tokenize(text)
  
      # Create stemmer object
      stemmer = SnowballStemmer('english')
  
      # Stem each token
      stemmed_words = [stemmer.stem(word) for word in tokens]
  
      # Remove punctuation
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
  
      # Remove stopwords
      meaningful_words = [word for word in clean_words if not word in stopwords.words('english')]
  
      # Create a SentimentIntensityAnalyzer object
      sid = SentimentIntensityAnalyzer()
  
      # Get the polarity score of the text
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
  
      return polarity_score
  
  assert sentiment_analyzer("I am happy today.") == {'neg': 0.0, 'neu': 0.312, 'pos': 0.688, 'compound': 0.6696}
  assert sentiment_analyzer("This is a terrible experience.") == {'neg': 0.602, 'neu': 0.398, 'pos': 0.0, 'compound': -0.5849}
  assert sentiment_analyzer("The movie was ok, nothing special.") == {'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'compound': 0.2263}
  assert sentiment_analyzer("") == {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}
output: def sentiment_analyzer(text):
      tokens = word_tokenize(text)
      stemmer = SnowballStemmer('english')
      stemmed_words = [stemmer.stem(word) for word in tokens]
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
      meaningful_words = [word for word in clean_words if not word in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score