Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9904
Title: What makes consumers unsatisfied with your products: Review analysis at a fine-grained level
Authors: Jin, J
Ji, P 
Kwong, CK 
Keywords: Conceptual design
Customer requirement
Product design
Review analysis
Sentiment analysis
Text mining
Issue Date: 2015
Publisher: Pergamon Press
Source: Engineering applications of artificial intelligence, 2015 How to cite?
Journal: Engineering applications of artificial intelligence 
Abstract: Online product reviews contain valuable information regarding customer requirements (CRs). Intelligent analysis of a large volume of online CRs attracts interest from researchers in various fields. However, many research studies only concern sentiment polarity in the product feature level. With these results, designers still need to read a list of reviews to absorb comprehensive CRs. In this research, online reviews are analyzed at a fine-grained level. In particular, aspects of product features and detailed reasons of consumers are extracted from online reviews to inform designers regarding what leads to unsatisfied opinions. This research starts from the identification of product features and the sentiment analysis with the help of pros and cons reviews. Next, the approach of conditional random fields is employed to detect aspects of product features and detailed reasons from online reviews jointly. In addition, a co-clustering algorithm is devised to group similar aspects and reasons to provide a concise description about CRs. Finally, utilizing customer reviews of six mobiles in Amazon.com, a case study is presented to illustrate how the proposed approaches benefit product designers in the elicitation of CRs by the analysis of online opinion data.
URI: http://hdl.handle.net/10397/9904
ISSN: 0952-1976
EISSN: 1873-6769
DOI: 10.1016/j.engappai.2015.05.006
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