input: Write a function to read in a tweet and remove all punctuation, special characters and stopwords. The function should return the cleaned tweet as a list of words. If the input tweet is empty, the function should return an empty list.
  import re
  import string
  from nltk.corpus import stopwords
  
  assert remove_noise("I'm so excited to be going to the #MuseumTour! #BigDay #travel")==['excited', 'going', 'MuseumTour', 'BigDay', 'travel']
  assert remove_noise("Hello, world!")==['Hello', 'world']
  assert remove_noise("Python is great, isn't it?")==['Python', 'great']
  assert remove_noise("")==[]
output: def remove_noise(tweet):
      if not tweet:
          return []
      tweet = tweet.translate(str.maketrans('', '', string.punctuation))
      words = re.findall(r'\b\w+\b', tweet)
      stop_words = set(stopwords.words('english'))
      cleaned_words = [word for word in words if word.lower() not in stop_words]
      return cleaned_words