def classify(text):
    words = set(word for word in negate_sequence(text) if word in features)
    if (len(words) == 0): return True
    # Probability that word occurs in pos documents
    pos_prob = sum(log((pos[word] + 1) / (2 * totals[0])) for word in words)
    neg_prob = sum(log((neg[word] + 1) / (2 * totals[1])) for word in words)
    return neg_prob > pos_prob
dataset/ETHPy150Open vivekn/sentiment/info.py/classify/swapped operands of `>`