DocumentCode
1781918
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
fYear
2014
fDate
2-3 Aug. 2014
Firstpage
249
Lastpage
254
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Enterprise Systems Conference (ES), 2014
Conference_Location
Shanghai
Print_ISBN
978-1-4799-5553-4
Type
conf
DOI
10.1109/ES.2014.38
Filename
6997054
Link To Document