With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the plethora of online offers. Thus, techniques for personalization and shopping assistance are in high demand by users, as well as by shopping platforms themselves. For a pleasant and successful shopping experience, users should be empowered to easily decide on which products to buy with high confidence. However, especially for entertainment goods like e.g. movies, books, or music, this task is very challenging. Unfortunately, todays approaches for dealing with this challenge (like e.g. recommender systems) suffer severe drawbacks: recommender systems are completely opaque, i.e. the recommendation is hard to justify semantically. User reviews could help users to form an opinion of recom-mended items, but with several thousand reviews available for e.g. a given popular movie, it is very challenging for users to find representative reviews. In this paper, we propose a novel technique for automatically analyzing user reviews using ad-vanced opinion mining techniques. The results of this analysis are then used to group reviews by their semantics, i.e. by their contained opinions and point-of-views. Furthermore, the rele-vant paragraphs with respect to each opinion is extracted and presented to the user. These extracts can easily be digested by users to allow them a quick and diverse forming of opinion, and thus increasing their confidence in their decision, and their overall customer satisfaction.