基于top-k显露模式的商品对比评论分析

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。

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

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.