摘要
结合机器学习方法中的SVM算法和KNN算法各自的优势,提出一种KSVM分类算法,采用具有语义倾向的词并综合其词性作为特征项,将一些网络评论进行情感分类,以判断一篇评论是正面还是反面.实验表明,运用该算法对网上的一些评论进行分类,可以达到较高的准确率.
A KSVM classification algorithm by combining the advantages of SVM algorithm and KNN algorithm in machine learning is proposed.Some with semantics tendency and combined the parts of speech is chosen as the characteristic items,and the proposed algorithm is applied in sentiment classification of network comments to judge one comment positive or negative.Experimental results showed that the proposed algorithm can classify some comments online with a higher accuracy.
出处
《郑州轻工业学院学报（自然科学版）》
CAS
2011年第3期1-4,共4页
Journal of Zhengzhou University of Light Industry:Natural Science
基金
河南省重点科技攻关项目(082102210054)
河南省自然科学基金资助项目(0411010500)
关键词
语义倾向度
情感文本分类
情感特征选择
KSVM
semantics tendency
sentiment classification
sentiment feature selection
KSVM
