input: Write a function to analyze the sentiment of a given text using tokenization, stemming, removal of stopwords and punctuation, and sentiment analysis. The function should return the polarity score of the text.
  
  
  import re
  from nltk.stem.snowball import SnowballStemmer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from nltk.sentiment.vader import SentimentIntensityAnalyzer 
  
  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 love this product!") == {'neg': 0.0, 'neu': 0.356, 'pos': 0.644, 'compound': 0.6249}
  assert sentiment_analyzer("This is terrible.") == {'neg': 0.575, 'neu': 0.425, 'pos': 0.0, 'compound': -0.5106}
  assert sentiment_analyzer("The weather is nice today!") == {'neg': 0.0, 'neu': 0.397, 'pos': 0.603, 'compound': 0.4215}
  assert sentiment_analyzer("I feel so-so about it.") == {'neg': 0.0, 'neu': 0.804, 'pos': 0.196, 'compound': 0.25}
  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