Phrase-Based Extraction Of User Opinions In Mobile App Reviews

Mobile app reviews often contain useful user opinions like bug reports or suggestions. However, looking for those opinions manually in thousands of reviews is ineffective and time-consuming. In this paper, we propose PUMA, an automated, phrase-based approach to extract user opinions in app reviews. Our approach includes a technique to extract phrases in reviews using part-of-speech (PoS) templates; a technique to cluster phrases having similar meanings (each cluster is considered as a major user opinion); and a technique to monitor phrase clusters with negative sentiments for their outbreaks over time. We used PUMA to study two popular apps and found that it can reveal severe problems of those apps reported in their user reviews.