Mining online product reviews to identify consumers´ fine-grained concerns

Online product reviews contain valuable information about customer requirements (CRs). Intelligent analysis of a large volume of online CRs attracts interest from researchers in different fields. However, many research studies only concern sentiment polarity in different level and designers still need to read these reviews to absorb comprehensive CRs. In this research, online reviews are analyzed to obtain consumers´ fine-grained concerns. Specifically, aspects of product features and detailed reasons of consumers are extracted from online reviews. 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 jointly. In addition, a co-clustering algorithm is devised to group similar aspects and reasons to provide concise descriptions about CRs. Finally, with hundreds of 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.