用于在线产品评论质量分析的Co-training算法

doi:10.3969/j.issn.1007-2861.2014.02.013

上海大学学报(自然科学版) ›› 2014, Vol. 20 ›› Issue (3): 289-295.doi: 10.3969/j.issn.1007-2861.2014.02.013

用于在线产品评论质量分析的Co-training算法

靳健1,2, 季平2,3

1. 北京师范大学政府管理学院, 北京100875; 2. 香港理工大学深圳研究院, 广东深圳518057; 3. 香港理工大学工业与系统工程系, 香港999077

收稿日期:2014-05-03 出版日期:2014-06-26 发布日期:2014-06-26
通讯作者: 季平(1961—), 男, 副教授, 博士, 研究方向为企业资源规划、运营管理、优化理论及其应用. E-mail:p.ji@polyu.edu.hk
作者简介:季平(1961—), 男, 副教授, 博士, 研究方向为企业资源规划、运营管理、优化理论及其应用. E-mail: p.ji@polyu.edu.hk
基金资助:
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)

Co-training Algorithm for Quality Analysis of Online Customer Reviews

JIN Jian1,2, JI Ping2,3

1. School of Government, Beijing Normal University, Beijing 100875, China;
2. Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, Guangdong, China;
3. Department of Industrial & Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China

Received:2014-05-03 Online:2014-06-26 Published:2014-06-26
Contact: 季平(1961—), 男, 副教授, 博士, 研究方向为企业资源规划、运营管理、优化理论及其应用. E-mail: p.ji@polyu.edu.hk E-mail:p.ji@polyu.edu.hk

摘要/Abstract

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.

关键词: Co-training算法, 产品设计, 评论质量, 文本挖掘, 在线产品评论, 数据质量

Abstract: 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 competition. Typically, the quality 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.

Key words: Co-training algorithm, online customer review, product design, quality of online reviews, text mining, data quality

中图分类号:

F 287.4

靳健1,2, 季平2,3. 用于在线产品评论质量分析的Co-training算法[J]. 上海大学学报(自然科学版), 2014, 20(3): 289-295.

JIN Jian1,2, JI Ping2,3. Co-training Algorithm for Quality Analysis of Online Customer Reviews[J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(3): 289-295.

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用于在线产品评论质量分析的Co-training算法

Co-training Algorithm for Quality Analysis of Online Customer Reviews

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