基于top-k显露模式的商品对比评论分析  

Analysis on distinguishing product reviews based on top-k emerging patterns

作　　者：刘璐[1] 王怡宁[1] 段磊[1,2] Jyrki Nummenmaa 晏力[1] 唐常杰[1] 

机构地区：[1]四川大学计算机学院,成都610065 [2]四川大学华西公共卫生学院,成都610041 [3]坦佩雷大学信息科学学院,芬兰坦佩雷fi33014

出　　处：《计算机应用》2015年第10期2727-2732,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61103042);中国博士后科学基金资助项目(2014M552371);软件工程国家重点实验室开放研究基金资助项目(SKLSE2012-09-32)

摘　　要：随着电子商务的发展,许多购物网站都提供商品评论作为用户购物的决策参考。由于商品评论具有海量、冗余、不规范的特点,用户难以在短时间内浏览所有商品评论,更难以基于评论内容发现商品对比特征。对此,设计了top-k显露模式挖掘算法,并将此算法应用于商品评论对比分析,实现了用户购物决策支持系统——Review Scope。Review Scope能够从不同商品的评论中发现特定商品的对比评论,并以此作为购物决策可视化地提供给用户。基于京东商城真实商品评论数据的实验结果表明Review Scope具有有效、灵活、用户友好的特点。With the development of e-commerce, online shopping Web sites provide reviews for helping a customer to make the best choice. However, the number of reviews is huge, and the content of reviews is typically redundant and non- standard. Thus, it is difficult for users to go through all reviews in a short time and find the distinguishing characteristics of a product from the reviews. To resolve this problem, a method to mine top-k emerging patterns was proposed and applied to mining reviews of different products. Based on the proposed method, a prototype, called ReviewSeope, was designed and implemented. ReviewSeope can find significant comments of certain goods as decision basis, and provide visualization results. The case study on real world data set of JD. corn demonstrates that ReviewSeope is effective, flexible and user-friendly.

关 键 词：商品评论 购物决策支持 模式可视化 显露模式挖掘 对比评论 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]