input: Write a function that reads in a tweet, removes all punctuation, special characters, and stopwords, and returns the cleaned tweet as a list of words. The function should return an empty list if the input tweet is empty.
  assert remove_bad_chars("I'm so excited to be going to the #MuseumTour! #BigDay #travel")==['excited', 'going', 'MuseumTour', 'BigDay', 'travel']
  assert remove_bad_chars("Hello, world!")==['Hello', 'world']
  assert remove_bad_chars("#Python #coding")==['Python', 'coding']
  assert remove_bad_chars("")==[]
output: def remove_bad_chars(tweet):
      import re
      from nltk.corpus import stopwords
      stop_words = set(stopwords.words('english'))
      words = re.findall(r'\b\w+\b', tweet)
      cleaned_words = [word for word in words if word.lower() not in stop_words]
      return cleaned_words