User feedback in the form of customer reviews, blogs, and forum posts is an essential feature of e-commerce. Users often read online product reviews to get an insight into the quality of various aspects of a product. Besides, users have different aspect preferences, and they look for reviews that contain relevant information regarding their preferred aspect(s). However, as reviews are unstructured and voluminous, it becomes exhaustive and laborious for users to find relevant reviews. Lack of domain knowledge about various aspects and sub-aspects of a product, and how they are related to each other, also add to the problem. Although this information could be there in product reviews, it is not easy for users to spot it instantly from the reviews. This paper seeks to address the above problems and presents two novel algorithms that summarize product reviews, and provides an interactive search interface, similar to popular faceted navigation. We solve the problem by creating an aspect ontology tree with high aspect extraction precision. © 2018 Elsevier B.V.