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Review on recent advances in information mining from big consumer opinion data for product design

Jin, Jian, Liu, Ying, Ji, Ping and Kwong, C. K. 2018. Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering 19 (1) , 010801. 10.1115/1.4041087

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Abstract

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: American Society of Mechanical Engineers (ASME)
ISSN: 1530-9827
Date of First Compliant Deposit: 31 July 2018
Date of Acceptance: 20 July 2018
Last Modified: 17 Jan 2019 17:12
URI: http://orca.cf.ac.uk/id/eprint/113766

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