Expanding Chinese Sentiment Dictionaries from Large Scale Unlabeled Corpus

Unsupervised sentiment classification usually needs a user defined sentiment dictionary. However, the existing dictionaries in Chinese are insufficient, for example, the intersection rate of two popular Chinese sentiment dictionaries HowNet and NTUSD is less than 10%. In this paper, we present a method to help expand the dictionaries with more sentiment words by ranking them through link analysis based on a word graph constructed from a large unlabeled corpus. Meanwhile, our method could compute a sentiment polarity strength for each word in the new dictionaries. Manual evaluation has shown that our method has a high precision to expand the dictionaries. Experiments for sentiment classification have shown that the new dictionaries with the polarity strength for each word given by our algorithm are effective to improve the performance. As a byproduct, our algorithm could also discover the errors existing in current dictionaries.