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. ; Department of Industrial and Systems Engineering ; Chinese Mainland Affairs Office