Intricate user-behaviors can be understood by discovering user interests from their reviews. Topic modeling techniques have been extensively explored to discover latent user interests from user reviews. However, a topic extracted by topic modelling techniques can be a mixture of several quite different concepts and thus less interpretable. In this paper, the authors present a method that uses topic modeling techniques to discover a large number of topics and applies hierarchical clustering to generate a much smaller number of interpretable User-Concerns. These User-Concerns are further compared with topics generated by Latent Dirichlet Allocation (LDA) and Pachinko Allocation Model (PAM) and shown to be more coherent and interpretable. The authors cut the linkage tree formed while performing the hierarchical clustering of the User-Concerns, at different levels, and generate a hierarchy of User-Concerns. They also discuss how collaborative filtering based recommendation systems can be enriched by infusing additional user-behavioral knowledge from such hierarchy.