Title :
Mining Customer Requirement from Helpful Online Reviews
Author :
Zhenping Zhang ; Jiayin Qi ; Ge Zhu
Author_Institution :
Sch. of Econ. & Manage., Beijing Univ. of Posts & Telecommun., Beijing, China
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.
Keywords :
Internet; consumer behaviour; data mining; marketing data processing; product design; Chinese mobile phone market; conjoint analysis; customer requirement mining; online review helpfulness; product online review; Algorithm design and analysis; Analytical models; Companies; Data mining; Feature extraction; Prediction algorithms; Principal component analysis; Kano model; Online review; brand; conjoint analysis; helpfulness; product design;
Conference_Titel :
Enterprise Systems Conference (ES), 2014
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-5553-4
