Customer knowledge discovery from online reviews

被引：21
|
作者：
You, Weijia [2 ]
Xia, Mu [1 ]
Liu, Lu [3 ]
Liu, Dan [3 ]
机构：
[1] Santa Clara Univ, Dept OMIS, Leavey Sch Business, Santa Clara, CA 95053 USA
[2] Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China
[3] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China
来源：
ELECTRONIC MARKETS | 2012年 / 22卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Customer knowledge management; Customer satisfaction; Text mining; Ordinal regression; WORD-OF-MOUTH; SATISFACTION; CONSUMER; ANTECEDENTS; SALES; DETERMINANTS; EXPECTATION; INTENTIONS; REPUTATION; INTERNET;
D O I：
10.1007/s12525-012-0098-y
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
The explosive growth of Chinese electronic market has made it possible for companies to better understand consumers' opinion towards their products in a timely fashion through their online reviews. This study proposes a framework for extracting knowledge from online reviews through text mining and econometric analysis. Specifically, we extract product features, detect topics, and identify determinants of customer satisfaction. An experiment on the online reviews from a Chinese leading B2C (Business-to-Customer) website demonstrated the feasibility of the proposed method. We also present some findings about the characteristics of Chinese reviewers.
引用
收藏
页码：131 / 142
页数：12
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