text: import nltk, os, pickle
  from sklearn.pipeline import Pipeline
  from tweet_analytics import *
  
  path = os.path.dirname(os.path.realpath(__file__))
  parent = os.path.dirname(path)
  cwd = parent + "/nltk_data"
  nltk.data.path = [cwd]
  
  custom_tweet = "I love it."
      # custom_tokens = remove_noise(word_tokenize(custom_tweet))
      # doc = ' '.join(custom_tokens)
  with open(path + '/trainedpipe.pkl', 'rb') as f:
      classifier = pickle.load(f)
  
  res = classifier.predict([custom_tweet])
  print(custom_tweet, "Positive" if res == 1 else "Negative")