Mining customer requirement from helpful online reviews

Zhenping Zhang, Jiayin Qi, Zhenping Zhang, Ge Zhu

Risultato della ricerca: Conference contribution

10 Citazioni (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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteProceedings - 2nd International Conference on Enterprise Systems, ES 2014
Pagine249-254
Numero di pagine6
Stato di pubblicazionePublished - 2014

All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.1800.1802???
  • ???subjectarea.asjc.1700.1710???

Fingerprint

Entra nei temi di ricerca di 'Mining customer requirement from helpful online reviews'. Insieme formano una fingerprint unica.

Cita questo