def binary_labels(df, min_set=None):

    # Remove outliers
    count = df['review_text'].str.len()
    df = df[(count > 3) | (count < 4000)].copy()

    # Equilize dataset sample size
    if min_set is None:
        min_set = df['rating'].value_counts().min() * 4

    # Split rating 1.0, 2.0 as negative reviews
    rating_1 = df[df['rating'] == 1.0].iloc[:min_set // 4]
    rating_2 = df[df['rating'] == 2.0].iloc[:min_set // 4]
    negative_reviews = pd.concat([rating_1, rating_2])
    negative_reviews['label'] = 0

    # Split rating 4.0, 5.0 as positive reviews
    rating_4 = df[df['rating'] == 4.0].iloc[:min_set // 4]
    rating_5 = df[df['rating'] == 5.0].iloc[:min_set // 4]
    positive_reviews = pd.concat([rating_4, rating_5])
    positive_reviews['label'] = 1

    dataset = pd.concat([negative_reviews, positive_reviews])
    dataset.sort_index(inplace=True)

    return dataset

dataset = binary_labels(df,  min_set=100000)