Cleaning Tweets with NLTK

import re
from string import punctuation
from nltk.corpus import stopwords
from nltk.tokenize import TweetTokenizer

punctuation += '΄´’…“”–—―»«' # string.punctuation misses these.

cache_english_stopwords = stopwords.words('english')

def tweet_clean(tweet):
    print('Original tweet:', tweet, '\n')
    # Remove HTML special entities (e.g. &)
    tweet_no_special_entities = re.sub(r'\&\w*;', '', tweet)
    print('No special entitites:', tweet_no_special_entities, '\n')
    # Remove tickers
    tweet_no_tickers = re.sub(r'\$\w*', '', tweet_no_special_entities)
    print('No tickers:', tweet_no_tickers, '\n')
    # Remove hyperlinks
    tweet_no_hyperlinks = re.sub(r'https?:\/\/.*\/\w*', '', tweet_no_tickers)
    print('No hyperlinks:', tweet_no_hyperlinks, '\n')
    # Remove hashtags
    tweet_no_hashtags = re.sub(r'#\w*', '', tweet_no_hyperlinks)
    print('No hashtags:', tweet_no_hashtags, '\n')
    # Remove Punctuation and split 's, 't, 've with a space for filter
    tweet_no_punctuation = re.sub(r'[' + punctuation.replace('@', '') + ']+', ' ', tweet_no_hashtags)
    print('No punctuation:', tweet_no_punctuation, '\n')
    # Remove words with 2 or fewer letters
    tweet_no_small_words = re.sub(r'\b\w{1,2}\b', '', tweet_no_punctuation)
    print('No small words:', tweet_no_small_words, '\n')
    # Remove whitespace (including new line characters)
    tweet_no_whitespace = re.sub(r'\s\s+', ' ', tweet_no_small_words)
    tweet_no_whitespace = tweet_no_whitespace.lstrip(' ') # Remove single space remaining at the front of the tweet.
    print('No whitespace:', tweet_no_whitespace, '\n')
    # Remove characters beyond Basic Multilingual Plane (BMP) of Unicode:
    tweet_no_emojis = ''.join(c for c in tweet_no_whitespace if c <= '\uFFFF') # Apart from emojis (plane 1), this also removes historic scripts and mathematical alphanumerics (also plane 1), ideographs (plane 2) and more.
    print('No emojis:', tweet_no_emojis, '\n')
    # Tokenize: Change to lowercase, reduce length and remove handles
    tknzr = TweetTokenizer(preserve_case=False, reduce_len=True, strip_handles=True) # reduce_len changes, for example, waaaaaayyyy to waaayyy.
    tw_list = tknzr.tokenize(tweet_no_emojis)
    print('Tweet tokenize:', tw_list, '\n')
    # Remove stopwords
    list_no_stopwords = [i for i in tw_list if i not in cache_english_stopwords]
    print('No stop words:', list_no_stopwords, '\n')
    # Final filtered tweet
    tweet_filtered =' '.join(list_no_stopwords)
    print('Final tweet:', tweet_filtered)

s = '    RT @Amila #Test\nTom\'s newly listed Co. &amp; Mary\'s unlisted     Group to supply tech for nlTK.\nh.. $TSLA $AAPL https:// t.co/x34afsfQsh'
tweet_clean(s)