input: Write a function to analyze the sentiment of a given text by tokenizing it, removing stopwords, stemming the words, and then calculating the polarity score using the SentimentIntensityAnalyzer. If the input text is empty, return a neutral sentiment score.
  
  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 very happy today!")=={'neg': 0.0, 'neu': 0.286, 'pos': 0.714, 'compound': 0.6114}
  assert sentiment_analyzer("I am really sad and disappointed.")=={'neg': 0.694, 'neu': 0.306, 'pos': 0.0, 'compound': -0.5779}
  assert sentiment_analyzer("")=={'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}
output: def sentiment_analyzer(text):
      if not text:
          return {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}
      
      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 word not in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      
      return polarity_score