def pre_process_data(filepath):
    '\n    This is dependent on your training data source but we will try to generalize it as best as possible.\n    '
    positive_path = os.path.join(filepath, 'pos')
    negative_path = os.path.join(filepath, 'neg')
    pos_label = 1
    neg_label = 0
    dataset = []
    for filename in glob.glob(os.path.join(positive_path, '*.txt')):
        with open(filename, 'r') as f:
            dataset.append((pos_label, f.read()))
    for filename in glob.glob(os.path.join(negative_path, '*.txt')):
        with open(filename, 'r') as f:
            dataset.append((neg_label, f.read()))
    shuffle(dataset)
    return dataset
This is dependent on your training data source but we will try to generalize it as best as possible.
def pre_process_data(filepath):
    '\n    \n    '
    positive_path = os.path.join(filepath, 'pos')
    negative_path = os.path.join(filepath, 'neg')
    pos_label = 1
    neg_label = 0
    dataset = []
    for filename in glob.glob(os.path.join(positive_path, '*.txt')):
        with open(filename, 'r') as f:
            dataset.append((pos_label, f.read()))
    for filename in glob.glob(os.path.join(negative_path, '*.txt')):
        with open(filename, 'r') as f:
            dataset.append((neg_label, f.read()))
    shuffle(dataset)
    return dataset