Abstract
Mobile application distribution platforms such as Google Play and Apple Store allow users to submit feedback in form of ratings and reviews towards downloaded apps, which actually serve as the communication channel between app users and developers. User reviews of mobile apps often contain complaints or suggestions that are valuable for developers to improve user experience and satisfaction. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this paper, we present CrowdApp, a novel computational framework that reexamines the impact of user reviews on mobile apps (app downloads, etc). Our approach explores multiple app aspects from user reviews, and further analyses the effects of different user feedback towards app downloads using the econometric method. Our econometric analysis reveals that user feedback has impact to app downloads. This work is an exploratory study that integrates econometric methodologies and text mining techniques towards a more complete analysis of the information captured from app reviews, and the results help app developers address the most complained app problems at an early stage.
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