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.
9781479955541
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/403701
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