Online reviews play a fundamental role in supporting purchase decisions and driving sales, but the sheer quantity and varying quality pose challenges for consumers to navigate. Using an unsupervised machine learning approach to extract latent topics from review texts, our paper demonstrates that shopping platforms can extract reviewers\u27 original purchasing goals (profiles) varying in the degree of utility and hedonic orientations. These profiles significantly alter their review behaviors in terms of effort, complexity, sentiment, and rating decision. A follow-up experiment finds early evidence that future consumers perceive reviews that match their shopping orientation more favorably in terms of both argument quality and review helpfulness. The paper contributes a new approach to understanding reviewer behaviors and makes a practical recommendation to online shopping platforms to match reviewer and consumer purchasing orientations
