code: def load_data_and_labels():
      
      # Load data from files
      pos_path = "./data/rt-polaritydata/rt-polarity.pos"
      neg_path = "./data/rt-polaritydata/rt-polarity.neg"
      if not os.path.exists(pos_path):
          os.system("git clone https://github.com/dennybritz/cnn-text-classification-tf.git")
          os.system('mv cnn-text-classification-tf/data .')
          os.system('rm -rf cnn-text-classification-tf')
      positive_examples = list(open(pos_path).readlines())
      positive_examples = [s.strip() for s in positive_examples]
      negative_examples = list(open(neg_path).readlines())
      negative_examples = [s.strip() for s in negative_examples]
      # Split by words
      x_text = positive_examples + negative_examples
      x_text = [clean_str(sent) for sent in x_text]
      x_text = [s.split(" ") for s in x_text]
      # Generate labels
      positive_labels = [1 for _ in positive_examples]
      negative_labels = [0 for _ in negative_examples]
      y = np.concatenate([positive_labels, negative_labels], 0)
      return [x_text, y]
docs: Loads MR polarity data from files, splits the data into words and generates labels.
      Returns split sentences and labels.