An Empirical Study of Sentiment Analysis for Chinese Microblogging

摘要：

This paper used three machine learning algorithms, three kinds of feature selection methods and three feature weight methods to study the sentiment classification for Chinese microblogging. The experimental results indicate that the performance of S VM 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 microblogging. Combining the three factors the conclusion can be drawn that the performance of combination of SVM, IG and TF-IDF is best.

关键词： microblogging sentiment analysis machine learning feature selection term weight

作者: Zhiming Liu LuLiu Hong Li

作者单位: School of Economics and Management, Beihang University, Beijing 100191, China

会议类型: 国际会议

会议名称: The Eleventh Wuhan International Conference on E-Business(第十一届武汉电子商务国际会议)

会议地点: 武汉

会议语种:英文

页码: 306-311

在线出版日期: 2012-05-26（万方平台首次上网日期，不代表论文的发表时间）