Mining customer requirement from helpful online reviews

Zhenping Zhang, Jiayin Qi, Zhenping Zhang, Ge Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Today there are a huge quantity of online reviews available across different categories of products. The key question is how to select helpful online reviews and what can we learn from the abundant reviews. In this paper, we first conclude five categories of features to predict reviews' helpfulness from the perspective of a product designer and then present an approach based on conjoint analysis to measure customer requirement. The suggested approach are demonstrated using product data from a popular Chinese mobile phone market.
Original languageEnglish
Title of host publicationProceedings - 2nd International Conference on Enterprise Systems, ES 2014
Pages249-254
Number of pages6
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Information Systems

Fingerprint

Dive into the research topics of 'Mining customer requirement from helpful online reviews'. Together they form a unique fingerprint.

Cite this