Empirical study of sentiment classification for Chinese microblog based on machine learning
With the development of microblog, it is more convenient to comment on the Web. Up to now, there are very few studies on the sentiment classification for Chinese microblog, therefore this paper uses three machine learning algorithms, three kinds of feature selection methods and three feature weight methods to study the sentiment classification for Chinese microblog. The experimental results indicate that the performance of SVM is best in three machine learning algorithms, IG is the better feature selection method compared to the other methods, and TF-IDF is best fit for the sentiment classification in Chinese microblog. Combining the three factors the conclusion can be drawn that the performance of combination of SVM, IG and TF-IDF is best. For the movie domain it is found that the sentiment classification depends on the review style.
国家科技期刊平台
NSTL
万方数据
维普
