5d6cdbd53544d057a1cf344526ec70e0995b77d1,newspaper/nlp.py,,keywords,#Any#,107

Before Change

            freq[word] += 1

        min_size = min(10, len(freq))
        keywords = tuple(freq.most_common(min_size))
        keywords = dict((x, y) for x, y in keywords)

        for k in keywords:
            articleScore = keywords[k]*1.0 / max(num_words, 1)
            keywords[k] = articleScore * 1.5 + 1

        keywords = sorted(iter(keywords.items()), key=operator.itemgetter(1))
        keywords.reverse()
        return dict(keywords)
    else:

After Change

                freq[word] = 1

        min_size = min(NUM_KEYWORDS, len(freq))
        keywords = sorted(freq.items(),
                          key=lambda x: (x[1], x[0]),
                          reverse=True)
        keywords = keywords[:min_size]
        keywords = dict((x, y) for x, y in keywords)

        for k in keywords:
            articleScore = keywords[k]*1.0 / max(num_words, 1)

Instances