Consumers are increasingly relying on other consumers’ online reviews of features and quality of products while making their purchase decisions. However, the rapid growth of online consumer product reviews makes browsing a large number of reviews and identifying information of interest time consuming and cognitively demanding. Although there has been extensive research on text review mining to address this information overload problem in the past decade, the majority of existing research mainly focuses on the quality of reviews and the impact of reviews on sales and marketing. Relatively little emphasis has been placed on mining reviews to meet personal needs of individual consumers. As an essential first step toward achieving this goal, this study proposes a product feature-oriented approach to the analysis of online consumer product reviews in order to support feature-based inquiries and summaries of consumer reviews. The proposed method combines LDA (Latent Dirichlet Allocation) and a synonym lexicon to extract product features from online consumer product reviews. Our empirical evaluation using consumer reviews of four products shows higher effectiveness of the proposed method for feature extraction in comparison to association rule mining.