Single Stage Prediction with Online Reviews for Mobile App Development and Management

Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represent a valuable source of consumer feedback on the app. We create a supervised topic modeling approach for app developers to use mobile reviews as useful sources of quality and customer feedback, thereby complementing traditional software testing. The approach is based on a constrained matrix factorization that leverages the relationship between term frequency and a given response variable in addition to co-occurrences between terms to recover topics that are both predictive of consumer sentiment and useful for understanding the underlying textual themes. The factorization can provide guidance on a single app’s performance as well as systematically compare different apps over time for benchmarking of features and consumer sentiment. We apply our approach using a dataset of over 81,000 mobile reviews over several years for the two of the most reviewed online travel agent apps from the iOS and Google Play marketplaces. 1 ar X iv :1 60 7. 07 51 5v 1 [ st at .A P] 2 6 Ju l 2 01 6