摘要
由于网络产品评论信息可以极大地影响产品的销售,因此很多产品评论人故意捧抬或诋毁特定产品来达到其目的。Wang G等人利用评论图中店铺、评论、评论人之间的相互关系,通过迭代计算得出评论、评论人和店铺的信誉度,从而发现虚假评论人。针对网络中无店铺的购物环境,提出了用产品替代店铺的新评论图结构,设计了一种逐步淘汰评论人及其评论的ICE算法,它极大地提高了迭代收敛速度。同时通过改进评论、评论人和产品的评分函数,进一步提高了基于评论图方法检测虚假评论人的准确度。实验表明,ICE算法不但收敛速度更快,而且具有更高的准确度。
Online product reviews can significantly affect product sales, resulting in a large number of reviewers who promote and/or demote target products by writing untruthful product reviews. Wang G et al proposed review graphs which reveal the relationships of reviews, reviewers and stores to calculate the reputations of reviews, reviewers and stores by convergent iterative computation, which can capture fake reviewers. To handle the storeless shopping environment, we proposed a new review graph structure by replacing stores with products, and designed a novel Algorithm ICE to fasten the iteration process by eliminating a certain portion of reviewers and reviews during each iteration. Meanwhile, by exploiting new scoring criteria for reviews, reviewers and products, the precision for identifying fake reviewers is also improved. Experiments show that the proposed Algorithm ICE not only performs faster but also more accurately than previous method. Keywords Fake review, Review graph,Opinion mining
出处
《计算机科学》
CSCD
北大核心
2014年第10期295-299,305,共6页
Computer Science
基金
国家自然科学基金(61373159)资助
关键词
虚假评论
评论图
观点挖掘
Fake review, Review graph, Opinion mining
