基于top-k显露模式的商品对比评论分析

doi:10.11772/j.issn.1001-9081.2015.10.2727

计算机应用 ›› 2015, Vol. 35 ›› Issue (10): 2727-2732.DOI: 10.11772/j.issn.1001-9081.2015.10.2727

• 第十五届中国机器学习会议(CCML2015)论文 • 上一篇下一篇

基于top-k显露模式的商品对比评论分析

刘璐¹, 王怡宁¹, 段磊^1,2, Jyrki Nummenmaa³, 晏力¹, 唐常杰¹

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 FI-33014

收稿日期:2015-06-15 修回日期:2015-06-26 发布日期:2015-10-14 出版日期:2015-10-10
通讯作者: 段磊(1981-),男,四川成都人,副教授,博士,主要研究方向:数据挖掘、知识发现,leiduan@scu.edu.cn
作者简介:刘璐(1991-),女,内蒙古五原人,硕士研究生,主要研究方向:数据挖掘、序列数据;王怡宁(1993-),女,内蒙古鄂尔多斯人,主要研究方向:数据挖掘;Jyrki Nummenmaa(1961-),男,芬兰人,教授,博士生导师,博士,主要研究方向:数据库、大数据处理;晏力(1988-),男,四川泸州人,硕士研究生,主要研究方向:数据挖掘;唐常杰(1946-),男,重庆人,教授,博士生导师,博士,主要研究方向:数据库、知识工程。
基金资助:
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)。

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

LIU Lu¹, WANG Yining¹, DUAN Lei^1,2, NUMMENMAA Jyrki³, YAN Li¹, TANG Changjie¹

1. School of Computer Science, Sichuan University, Chengdu Sichuan 610065, China;
2. West China School of Public Health, Sichuan University, Chengdu Sichuan 610041, China;
3. School of Information Sciences, University of Tampere, Tampere FI-33014, Finland

Received:2015-06-15 Revised:2015-06-26 Online:2015-10-14 Published:2015-10-10

摘要/Abstract

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——ReviewScope。ReviewScope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ReviewScope具有有效、灵活、用户友好的特点。

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

Abstract: 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 ReviewScope, was designed and implemented. ReviewScope can find significant comments of certain goods as decision basis, and provide visualization results. The case study on real world data set of JD.com demonstrates that ReviewScope is effective, flexible and user-friendly.

Key words: product review, shopping decision support, pattern visualization, emerging pattern mining, contrastive review

中图分类号:

TP311.13

刘璐, 王怡宁, 段磊, Jyrki Nummenmaa, 晏力, 唐常杰. 基于top-k显露模式的商品对比评论分析[J]. 计算机应用, 2015, 35(10): 2727-2732.

LIU Lu, WANG Yining, DUAN Lei, NUMMENMAA Jyrki, YAN Li, TANG Changjie. Analysis on distinguishing product reviews based on top-k emerging patterns[J]. Journal of Computer Applications, 2015, 35(10): 2727-2732.

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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

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