A Lexical Updating Algorithm for Sentiment Analysis on Chinese Movie Reviews

With the prevalence of Internet, sentiment analysis gets popularity among the world. Researchers have made use of kinds of online documents like commodities reivews and movie reviews as training samples to train their models and classfiers, by which they could speculate the underlying emotion in new ones. Douban is a Chinese online community where users share their personal reviews to express their feelings about movies. Those Chinese movie reviews were utilized by us to train our lexicon-based model. Yet multiple words in a ready-made lexicon do not agree with the movie reviews in a specific domain, which means the original lexicon acquires being updated to gain higher accuracy. In this paper we introduce a lexical updating algorithm based on a widely used lexicon. After turns of training of updating, this lexicon is capable of classifying sentiment among movie reviews. The experimental result shows our model using the updated lexicon could get a better performance than the primitive lexicon-based model.