摘要
在线评论广泛存在于电子商务网站平台,其中包含着客户对产品的评价及偏好.高效分析在线评论数据并满足客户需求,对许多谋求立足于竞争激烈的国际化市场的企业来说至关重要.但因在线评论的质量不一,使得如何分析在线评论的质量成为一项重要工作.从两个方面提取特征对在线评论进行描述,并构建了一种Co-training算法来判断评论的质量.通过对比实验验证了该算法相对于单一分类算法的优势.
Online customer reviews exist widely on e-commerce websites. Customer concerns and preferences about products are involved in these reviews. It is critical for business professionals to analyze online reviews efficiently and effectively in the fierce market com- petition. Typically, the quMity analysis of online reviews is a good example. Accordingly, two aspects of features are identified from online reviews, and a Co-training algorithm is built to analyze quality of online reviews. Effectiveness of the algorithm and its advantages over a single classification/regression algorithm is confirmed by experiments.
出处
《上海大学学报（自然科学版）》
CAS
CSCD
北大核心
2014年第3期289-295,共7页
Journal of Shanghai University:Natural Science Edition
基金
国家自然科学基金资助项目(71271185)
中央高校基本科研业务费专项资金资助项目(SKZZX2013091)
