摘要
为了提高情感特征提取的准确率,为高性能情感分析打下坚实的基础,提出了一种融合粗糙集与信息增益的情感特征选择方法.该方法借助信息增益判据选出高相关性的特征子集,再通过粗糙集剔除高冗余性的特征,从而得到最优的特征子集.在多个数据集上的测试表明,该方法可将若干经典方法的准确率提高4~9个百分点,是一种优秀的特征选择方法,对提升情感分析的整体性能有明显意义.
A Rough Set and Information Gain based on sentiment feature selection method is proposed for building a solid foundation in sentiment analysis.The novel method firstly uses Information Gain to select a feature subset which has high relativity with the class attribute.Secondly,the features which have high redundancy will be eliminated by Rough Set.Experimental results on several datasets reveal the method makes accuracy increase 4-9percentages than other methods.It is an outstanding feature selection method and has significance in sentiment analysis.
出处
《微电子学与计算机》
CSCD
北大核心
2016年第1期96-99,共4页
Microelectronics & Computer
基金
国家自然科学基金(61152003)
国家档案局科技计划项目(2014-X-65)
四川省教育厅科研项目(15ZB0323)
四川文理学院智能计算与物联网工程技术中心资助项目
关键词
情感分析
特征选择
粗糙集
信息增益
sentiment analysis
feature selection
Rough Set
Information Gain
