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.
Zhang Z., Qi J., Zhu G. (2014). Mining customer requirement from helpful online reviews. In Proceedings - 2nd International Conference on Enterprise Systems, ES 2014 (pp. 249-254). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/ES.2014.38].
Mining customer requirement from helpful online reviews
Zhang Z.;
2014-01-01
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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
Mining Customer Requirement From Helpful Online Reviews-for ICES 2014-final version.pdf
Solo gestori archvio
Tipologia:
Post-print
Dimensione
375.29 kB
Formato
Adobe PDF
|
375.29 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
