摘要
【目的】从大量在线商品评论中筛选出可信的评论辅助消费者制定购买决策。【方法】提出一种基于大数据思维的主流特征观点对的概念,依据特征观点对在不同用户评论中的认可程度,建立评论可信性排序模型。【结果】淘宝、天猫和京东平台的商品评论的主流特征观点对是稳定的;与已有模型相比,使用本文模型排序过的用户评论包含的产品特征范围更广,评论有用性提升7.5%,更能够反映评论的真实情况。【局限】仅从评论包含的特征观点对考虑评论可信性,而未考虑评论的具体语义情况。【结论】包含主流特征观点对数量越多的评论,其可信度则越大。
[Objective] This paper tries to choose credible comments from a large number of online product reviews, aiming to help consumers make purchasing decisions. [Methods] First, we proposed a concept of mainstream feature-opinion pair with the help of big data. Then, we established the credibility ranking model based on the recognition level of feature-opinion pair from different users＇ comments. [Results] We found that the mainstream feature-opinions of online product reviews were relatively stable among the users of Taobao, TMall and Jingdong. Compared with existing models, the reviews sorted by our method covered more product features, and their helpfulness was increased by 7.5%. [Limitations] We did not consider the specific semantic situation of the comments while ranking their credibility. [Conclusions] The more mainstream feature-opinion pairs each comment contains, the more credible it is.
出处
《数据分析与知识发现》
CSSCI
CSCD
2017年第10期32-42,共11页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目"C2C市场中基于行为树的销量识别与发布研究"(项目编号:71371012)
教育部人文社会科学研究一般项目"C2C市场中基于参与者行为的‘打榜’识别模型与应用研究"(项目编号:13YJA630098)的研究成果之一
关键词
在线商品评论
特征观点对
可信度
Online Product Reviews Feature-Opinion Pair Credibility
