A Review Selection Method for Finding an Informative Subset from Online Reviews
A Review Selection Method for Finding an Informative Subset from Online Reviews
复制标题
一种从在线评论中查找信息子集的评论选择方法
DOI：
10.1287/ijoc.2019.0950
复制
发表时间：
2020-06
影响因子：
2.1
通讯作者：
Chen Guoqing
中科院分区：
文献类型：
--
作者：
Zhang Jin;Wang Cong;Chen Guoqing
针对网络评论中的信息过载问题，提出了一种新的评论选择问题--信息评论子集选择问题（Informative Review Subset Selection problem，简称IRSS），并证明了该问题是NP-难求解和近似的。此外，提出了一种新的启发式方法（Combined Search-ComS）来求解该问题，并根据互信息熵选择与原始评论语料一致的评论子集。所提出的方法，然后通过广泛的数据实验和亚马逊数据的用户研究进行全面检查。实验结果表明，该方法与现有的其他方法相比，具有较好的整体性能，是一种有效的在线评论信息子集选择方法。所提出的方法被认为是可取的和有用的在线消费者和服务提供商。
Concerning the information overload of online reviews, this paper models a new review selection problem called Informative Review Subset Selection problem (namely, IRSS), and demonstrates that it is NP-hard to solve and approximate. Furthermore, a novel heuristic method (namely, Combined Search-ComS) is proposed for seeking the solution to the problem and selecting a subset of reviews, which is consistent with the original review corpus in light of mutual information entropy. The proposed method is then comprehensively examined via extensive data experiments and a user study on Amazon data. Experimental results reveal the overall superiority of the proposed method in comparison with other extant methods of concern, showing that it is an eective way to select an informative subset of online reviews. The proposed method is deemed desirable and useful for online consumers and service providers.