多级中文文本情感分类算法研究  

Study on hierarchical text sentiment classification algorithm

作　　者：邢玉娟[1] 李恒杰[1] 曹晓丽[1] 张成文[1] 

机构地区：[1]甘肃联合大学电子信息工程学院,兰州730000

出　　处：《计算机工程与应用》2012年第33期132-135,152,共5页Computer Engineering and Applications

基　　金：甘肃省教育厅基金项目(No.1113-01);甘肃联合大学科研高水平成果项目(No.2011GSP01)

摘　　要：针对文本情感分类准确率不高的问题,提出基于CCA-VSM分类器和KFD的多级文本情感分类方法。采用典型相关性分析对文档的权重特征向量和词性特征向量进行降维,在约简向量集上构建向量空间模型,根据模型之间的差异度设计VSM分类器,筛选出与测试文档差异度较小的R个模型作为核Fisher判别的输入,最终判别出文档的情感观点。实验结果表明:该方法比传统支持向量机有较高的分类准确率和较快的分类速度,权重特征和词性特征对分类准确率的影响较大。A novel hierarchical text sentiment classification approach based on CCA-VSM classifier and kernel Fisher discriminant is proposed to improve classification accuracy.CCA is utilized to reduce the dimensionality of feature vectors.And then vector space model is built on reduced vector set.By doing this,a novel CCA-VSM classifier is proposed according to the diversity between VSM models.R models,which possess smaller diversity,would be selected by CCA-VSM classifier.Kernel Fisher discriminant is used to make judgment.Experiment results show that hierarchical classifier is superior to SVM in text sentiment classification problem,and also show that the method of weight computation and the rule of parts of speech feature selection have big effection on classification results.

关 键 词：文本情感分类 核FISHER判别 支持向量机 向量空间模型 相关性分析 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]