Context: Users can have conflicting opinions and different experiences when using software and user reviews serve as a channel in which users can document their opinions and experiences. To develop and evolve software that is usable and relevant for a diverse group of users, different opinions and experiences need to be taken into account. Goal: In this paper we present DIVERSE, a feature and sentiment centric retrieval approach which automatically provides developers with a diverse sample of user reviews that is representative of the different opinions and experiences mentioned in the whole set of reviews. Results: We evaluated the diversity retrieval performance of our approach on reviews from seven apps from two different app stores. We compared the reviews retrieved by DIVERSE with a feature-based retrieval approach and found that on average DIVERSE outperforms the baseline approach. Additionally, a controlled experiment revealed that DIVERSE can help develop- ers save time when analyzing user reviews and was considered useful for detecting conflicting opinions and software evolution. Conclusions: DIVERSE can therefore help developers collect a comprehensive set of reviews and aid in the detection of conflicting opinions.