Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s12525-012-0098-y | Electronic Markets |
Keywords | Field | DocType |
Customer knowledge management, Customer satisfaction, Text mining, Ordinal regression, M1 | Customer intelligence,Data science,Customer satisfaction,Economics,Voice of the customer,Customer knowledge management,Ordinal regression,Customer advocacy,Econometric analysis,Marketing,Customer knowledge | Journal |
Volume | Issue | ISSN |
22 | 3 | 1422-8890 |
Citations | PageRank | References |
6 | 0.45 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijia You | 1 | 26 | 2.81 |
| Mu Xia | 2 | 238 | 12.95 |
| Lu Liu | 3 | 215 | 27.61 |
| Dan Liu | 4 | 25 | 8.89 |