一种新的中文词语情感极性判别方法  

A Novel Method to Compute Chinese Word Sentiment Polarity

作　　者：魏韡[1,2] 向阳[2] 

机构地区：[1]井冈山大学电子与信息工程学院,江西吉安343009 [2]同济大学电子与信息工程学院,上海201804

出　　处：《微电子学与计算机》2013年第5期84-86,91,共4页Microelectronics & Computer

基　　金：国家自然科学基金项目(61103069;71171148);江西省教育厅青年科学基金项目(GJJ11178);吉安市软科学计划项目(吉市科计字[2012]32-3)

摘　　要：词语的情感极性判别是文本情感分析的前提和基础.本文针对如何判别中文词语的情感极性,提出了一种基于图的半监督学习方法.该方法将词语看成图中的节点,链接节点的边的权重由《知网》2008提供的词语间的语义相似度决定.少量已知情感极性的词语作为标签,通过标签传递算法来判别大量未知词语的情感极性.实验结果表明该方法比传统的基于知网的词语情感极性判别方法有较高的正确率.Judging the semantic orientation of words is the basic work of the sentiment analysis of text.This paper proposes a graph—based semi—supervised learning to determine the sentiment polarity of Chinese words.The method constructs the graph,whose nodes consist of Chinese words.The weight of two nodes is based on the semantic similarity of two words in HowNet 2008.With the few labeled sentiment words,the polarity of many unlabeled words can be acquired using label propagation algorithm.Experimental results show that proposed method has higher degree of accuracy compared the traditional methods based on HowNet to judging sentiment polarity of words.

关 键 词：情感极性 半监督学习 图 知网 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]