input: Write a function to analyze the sentiment of a given text using the SentimentIntensityAnalyzer from the NLTK library. The function should tokenize the text, stem each token, remove punctuation and stopwords, and then return the polarity score of the processed text. If the input text is empty, return an empty dictionary.
  
  def sentiment_analyzer(text):
      # Tokenize the text
      if not text:
          return {}
      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 love this movie!") == {'neg': 0.0, 'neu': 0.295, 'pos': 0.705, 'compound': 0.8012}
  assert sentiment_analyzer("This is the worst experience ever.") == {'neg': 0.542, 'neu': 0.458, 'pos': 0.0, 'compound': -0.7717}
  assert sentiment_analyzer("It's okay, I guess.") == {'neg': 0.0, 'neu': 0.731, 'pos': 0.269, 'compound': 0.3802}
  assert sentiment_analyzer("") == {}
output: def sentiment_analyzer(text):
      if not text:
          return {}
      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