input: Write a function to analyze the sentiment of a given text using various text processing techniques such as tokenization, stemming, punctuation removal, stopwords removal, and sentiment analysis. If the provided text is empty, return a sentiment score of neutral (0).
  
  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')]
  
      # If the text is empty, return a neutral sentiment score
      if not meaningful_words:
          return {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}
  
      # 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 product!") == {'neg': 0.0, 'neu': 0.362, 'pos': 0.638, 'compound': 0.6249}
  assert sentiment_analyzer("I hate this product!") == {'neg': 0.558, 'neu': 0.442, 'pos': 0.0, 'compound': -0.5423}
  assert sentiment_analyzer("This product is neutral.") == {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
  assert sentiment_analyzer("") == {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}
output: 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')]
  
      # If the text is empty, return a neutral sentiment score
      if not meaningful_words:
          return {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}
  
      # Create a SentimentIntensityAnalyzer object
      sid = SentimentIntensityAnalyzer()
  
      # Get the polarity score of the text
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
  
      return polarity_score