old_contents: from sklearn.datasets import load_files
  from sklearn.feature_extraction.text import CountVectorizer
  
  
  categories = ['neg', 'pos']
  
  twitter_train = load_files('./twitter_data/twitter_data-train', categories=categories, load_content=True, shuffle=True, random_state=42)
  
  count_vect = CountVectorizer()
  X_train_counts = count_vect.fit_transform(twitter_train.data)
  print(X_train_counts.shape)
new_contents: from sklearn.datasets import load_files
  from sklearn.feature_extraction.text import CountVectorizer
  
  
  categories = ['neg', 'pos']
  
  twitter_train = load_files('./twitter_data/twitter_data-train', categories=categories, load_content=True, shuffle=True, random_state=42)
  
  #Ignoring decode errors may harm our results, but at least it works now
  count_vect = CountVectorizer(decode_error='ignore')
  X_train_counts = count_vect.fit_transform(twitter_train.data)
  print(X_train_counts.shape)
subject: Make vectorizer Ignore decode errors