基于评价修饰分布差的评论文本倾向性识别方法  

Comment Text Orientation Identification Method Based on Evaluation Modification Distribution Difference

作　　者：冯旭鹏[1] 马震[1] 谢波[1] 刘利军[2] 黄青松[2,3] 

机构地区：[1]昆明理工大学教育技术与网络中心,昆明650500 [2]昆明理工大学信息工程与自动化学院,昆明650500 [3]云南省计算机技术应用重点实验室,昆明650500

出　　处：《计算机工程》2016年第10期176-180,186,共6页Computer Engineering

基　　金：国家自然科学基金资助项目(81360230;81560296)

摘　　要：针对文本倾向性分类时因情感指向不明导致的修饰词极性误判和隐藏观点遗漏等问题,提出基于评价修饰分布差的倾向识别方法。建立修饰关系二部图和修饰分布向量,计算评价对象在正、负训练语料中被修饰词用于修饰的分布差异,提取修饰分布差异明显的特征,并将正、负修饰差异信息融入特征值的计算中。实验结果表明,相比抽取带有主观情感词作为特征进行支持向量机二类分类的倾向性识别方法,所提方法的分类准确率和召回率分别提高约4.6%和5.6%,可有效改善评论文本倾向性识别的效果。在面对跨领域情况时,分类准确率和召回率的降低幅度比抽取带有主观情感词作为特征进行支持向量机的二类分类减少约6.6%和6.4%,具有一定的领域适应性。In text orientation classification, there is a problem of adjunct words＇ polarity misjudgment and missing hidden view caused by unknown emotional direction. This paper proposes an orientation identification method based on evaluation modification distribution difference. It builds modification relationship bipartite graph and modification distribution vector, computes the modification distribution difference of evaluation object in positive and negative training corpus, extracts the features which have obvious difference, and integrates the positive and negative difference information into the process of characteristic value computation. Experimental results show that the proposed method can improve the precision and recall by 4.6% and 5.6% respectively for text orientation identification compared with other approaches which tend to pick the strong subjective emotional items as features and classify by Support Vector Machine （SVM） algorithm. The method has domain adaptability that it can reduce the decline of precision and recall by 6.6% and 6.4% in the cross-domain situation, when compared with the approaches which pick the emotional items as features and classify by SVM algorithm.

关 键 词：文本倾向识别 评价对象 修饰关系 分布差异 领域适应 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]