new_contents: import pandas as pd
  import os
  
  def strip_quotations_newline(text):
      text = text.rstrip()
      if text[0] == '"':
          text = text[1:]
      if text[-1] == '"':
          text = text[:-1]
      return text
  
  def expand_around_chars(text, characters):
      for char in characters:
          text = text.replace(char, " "+char+" ")
      return text
  
  def split_text(text):
      text = strip_quotations_newline(text)
      text = expand_around_chars(text, '".,()[]{}:;')
      splitted_text = text.split(" ")
      cleaned_text = [x for x in splitted_text if len(x)>1]
      text_lowercase = [x.lower() for x in cleaned_text]
      return text_lowercase
    
  
  def amazon_reviews():
      datafolder = '../datasets/amazon/'
      files = os.listdir(datafolder)
      Y_train, Y_test, X_train, X_test,  = [], [], [], []
      for file in files:
          f = open(datafolder + file, 'rb')
          label = file
          lines = f.readlines()
          no_lines = len(lines)
          no_training_examples = int(0.7*no_lines)
          for line in lines[:no_training_examples]:
              Y_train.append(label)
              X_train.append(split_text(line))
          for line in lines[no_training_examples:]:
              Y_test.append(label)
              X_test.append(split_text(line))
          f.close()
      return X_train, Y_train, X_test, Y_test
  
  
  def adult():
      datafile = '../datasets/adult/adult.data'
      file_test = '../datasets/adult/adult.test'
      df = pd.read_csv(datafile, header=None)
      Y_train = df[14].values
      del df[14]
      del df[2]
      X_train = df.values
  
      df_test = pd.read_csv(file_test, header=None)
      Y_test = df_test[14].values
      del df_test[14]
      del df_test[2]
      X_test = df_test.values
      return X_train, Y_train, X_test, Y_test
  
subject: make seperate file for easy loading of datasets