Sentiment Analysis of Chinese Micro-blog based on Classification and Rich Features

摘要：

　　With the development of the Web2.0, micro-blogs gradually become a common essential part of the public life.The reviews in the micro-blogs have huge hidden value.Many machine learning approaches have been used to solve sentiment analysis.However, the features used in existing researches are still not enough.To improve the accuracy of sentiment analysis, in this paper, we use a classification approach to solve two tasks of sentiment analysis: identifying opinion sentence and judging sentiment polarity of the emotional sentence.And we incorporate five kinds of features: sentiment lexicons-based features, NPOS(part of speech combination)-based features, pattern-based features, special symbols-based features and length-based features to train seven classifiers and compare their performance.Experimental result shows that Random Forest classifier achieves the best performance.

关键词： sentiment analysis Chinese micro-blog multiple features classification

作者: Jiayuan Ding Yongquan Dong Tongfei Gao Zichen Zhang Yali Liu

作者单位: School of Computer Science & Technology Jiangsu Normal University Xuzhou, China

会议类型: 国际会议

会议名称: The 13th Web Information Systems and Applications Conference(第十三届全国web信息系统及其应用学术会议)(WISA2016)、The 1st Symposium on Big Data Processing and Analysis)( BDPA 2016)第一届全国大数据处理与分析学术研讨会、The 1st Workshop on Information System Security)(ISS2016)(第一届全国信息系统安全研讨会

会议地点: 武汉

会议语种:英文

页码: 61-66

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）