Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62329
Title: Understanding big consumer opinion data for market-driven product design
Authors: Jin, J
Liu, Y
Ji, P 
Liu, H
Keywords: Big data
Customer requirement
Sentiment analysis
Product comparison
Trends analysis
Product design
Conceptual design
Text mining
Issue Date: 2016
Publisher: Taylor & Francis
Source: International journal of production research, 2016, v. 54, no. 10, p. 3019-3041 How to cite?
Journal: International journal of production research 
Abstract: Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or 4Vs', lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design.
URI: http://hdl.handle.net/10397/62329
ISSN: 0020-7543
EISSN: 1366-588X
DOI: 10.1080/00207543.2016.1154208
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