基于机器学习的中文微博情感分类实证研究

使用三种机器学习算法、三种特征选取算法以及三种特征项权重计算方法对微博进行了情感分类的实证研究。实验结果表明,针对不同的特征权重计算方法,支持向量机(SVM)和贝叶斯分类算法(Nave Bayes)各有优势,信息增益(IG)特征选取方法相比于其他的方法效果明显要好。综合考虑三种因素,采用SVM和IG,以及TF-IDF(Term Frequency-Inverse Document Frequency)作为特征项权重,三者结合对微博的情感分类效果最好。针对电影领域,比较了微博评论和普通评论之间分类模型的通用性,实验结果表明情感分类性能依赖于评论的风格。

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.