input: Write a function to create a sentiment analysis script that tokenizes the input text, stems the words, removes stopwords and punctuation, and returns the polarity score of the text. If the input text is empty, return a default polarity score of {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}.
  
  def sentiment_analyzer(text):
      if text.strip() == "":
          return {'neg': 0.0, 'neu': 1.0, '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 not word in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score
      
  assert sentiment_analyzer("I love programming.")=={'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.6369}
  assert sentiment_analyzer("This is a terrible day.")=={'neg': 0.646, 'neu': 0.354, 'pos': 0.0, 'compound': -0.7184}
  assert sentiment_analyzer("")=={'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
  assert sentiment_analyzer("The movie was average, neither great nor bad.")=={'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
  assert sentiment_analyzer("Absolutely fantastic!")=={'neg': 0.0, 'neu': 0.278, 'pos': 0.722, 'compound': 0.8519}
output: def sentiment_analyzer(text):
      if text.strip() == "":
          return {'neg': 0.0, 'neu': 1.0, '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 not word in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score