Online consumer-generated product reviews are a growing phenomenon and have led to the posting of colossal amounts of data by consumers on the Web. These data include consumers' thoughts, opinions, and feelings about brands and offer firms the opportunity to "listen in" on consumers to get a better understanding of the topics discussed about their brands. Using the human associative memory model as the theoretical framework, the authors introduce an approach to convert online product reviews into meaningful information about brand images using a novel combination of text mining and network analysis methodologies. Following a network-based understanding of brand image, the authors use online product reviews to extract consumers' brand associations and their interconnections as well as to depict and characterize the network of brand associations. In an empirical study, the authors test the approach and illustrate its managerial usefulness. The suggested approach allows managers to effectively monitor and detect strengths and weaknesses of brand image. Moreover, the proposed approach is one of the first attempts to measure brand image using consumer-generated content by applying text mining and network analysis.