摘要
对在线中文商品评论中可信度较低的评论信息进行过滤,为消费者提供对制定购买决策有帮助的评论。在深入分析在线中文商品评论特点的基础上,结合相关研究成果,通过问卷调查进行可信度影响因素的实证分析。根据实证结果,选取内容完整性、情感平衡性、评论时效性以及发布者身份明确性4类特征,采用CRFs模型进行评论可信度4级分类,并进行特征组合实验,得到最佳特征组合。实验效果显著,分类模型正确率均在75%以上。该研究成果可以用于改善现有的"人工效用评价"方式,为在线评论的优化过滤提供一种新的方法与思路。
This paper aims at filtering the lower credible online Chinese product reviews to offer valuable reviews for con- sumers＇ purchase decision. Based on the deep analysis of the online Chinese product reviews＇ characteristics, also with some related works, the authors make an empirical analysis on the credibility factors through questionnaires. According to the results of the empirical analysis, the authors select content integrity, emotional balance, review timeliness and clarity of the identity of the publisher as four features, use CRFs as reviews credibility＇ s classification model, and conduct fea- ture combination experiments to get the best feature combination. The experiments achieve significant results, and the cor- rect rates of the classification model are all above 75 %. The research results of this paper can improve the existing artifi- cial effectiveness evaluation method, thus offering new methods and thoughts for optimized filtering of the online reviews.
出处
《现代图书情报技术》
CSSCI
北大核心
2013年第9期60-66,共7页
New Technology of Library and Information Service
基金
国家自然科学基金项目"基于文本语义挖掘的商品评论信息可信度分析研究"(项目编号:71103085)的研究成果之一
关键词
在线商品评论
可信度
CRFs模型
影响因素
效用评价
Online product reviews Credibility CRFs model Affecting factor Effectiveness evaluation
