Online review, a form of customers’ feedback on E-commerce, has become an important channel for both consumers and producers to provide product information from a customer’s perspective (Park and Lee 2009, Zhu and Zhang 2010). It has been witnessed that Web 2.0 technologies have attracted an increasing number of people with various backgrounds to become active online writers and viewers (Cheng et al. 2012). As a result, a great number of reviews have been generated with different motivations (Kraut and Resnick 2012), in which the reviewing behaviors are more diverse and the language words used in reviewed contents are sparser than those generated by people who have similar backgrounds and a pure motivation for sharing information of product quality. Therefore, to understand information more precisely from massive and various online reviews, a feasible way is not only to explore what users said, but also why they said. However, in real applications, reviewers will not explain why they post reviews online, especially for those extrinsic reasons – such as status, financial reward, or social influence. Fortunately, part of a reviewer’s motivations can be observed by her/his reviewing behaviors, such as reviewing quality (posting a long or short review), promptness (posting a quick or lazy review), and attitude (posting review actively or passively) (Liu et al. 2008). By observing actual reviewing behaviors, we can infer immediate reasons why they review, even if they only write occasional reviews (Brown 2012). For example, if one were offended, or staffs were rude in online shopping process, people might take a quick reaction to express a grievance or warn others. Thus, the task of exploring reviewers’ behaviors (how they said) and further understanding reviewers’ exact opinions from massive and sparse online reviews becomes more important, especially, when these reviews are associated with some specific reviewing behaviors. In literature, most research has focused on mining the contents of reviews, for opinion (feature) extraction (Dave et al. 2003, Hu and Liu 2004, Pang and Lee 2008, Zhang et al. 2010), sentiment analysis (Cui 2006, Pang and Lee 2008), collaborative filtering (Zhang et al. 2014, Almahairi et al. 2015), and sales forecasting (Chintagunta et al. 2010, Yu et al. 2012). Whereas, customers’ reviewing behaviors have been overlooked. In this work, we employ the reviewing behavior dynamics method and the review-feature-based opinion mining method to explore the relationship between people’s reviewing manners (i.e., timely) and their reviewing opinions (what they talk about). The main contributions of this paper lie in two aspects. First, we present an analytical framework to explore the customers’ reviewing behavior dynamics. Second, we present a review-to-feature mapping method to solve the opinion mining problem for exploring the aspects from a novel perspective of customer purchasing-reviewing behavior similarity. The rest of this paper is organized as follows. Section 2 presents related work. Section 3 sketches out the methodology in detail. Section 4 shows and discusses the experimental results. Section 5 summarizes some managerial insights and Section 6 concludes the paper.