They'll Know It When They See It: Analyzing Post-Release Feedback from the Android Community

TL;DR

This paper uses natural language processing to analyze user feedback from the Android community, revealing common topics, user dissatisfaction with Jellybean, and correlated feature requests, highlighting challenges in prioritization.

Contribution

It introduces an NLP-based approach to analyze large-scale post-release feedback, identifying key improvement areas and user sentiment patterns in the Android ecosystem.

Findings

Features related to specific topics were most frequently requested.

Users expressed particular discontent with the Jellybean release.

End-users often requested improvements to related issues together.

Abstract

It is known that user involvement and user-centered design enhance system acceptance, particularly when end-users' views are considered early in the process. However, the increasingly common method of system deployment, through frequent releases via an online application distribution platform, relies more on post-release feedback from a virtual community. Such feedback may be received from large and diverse communities of users, posing challenges to developers in terms of extracting and identifying the most pressing requests to address. In seeking to tackle these challenges we have used natural language processing techniques to study enhancement requests logged by the Android community. We observe that features associated with a specific subset of topics were most frequently requested for improvement, and that end-users expressed particular discontent with the Jellybean release. End-users also tended to request improvements to specific issues together, potentially posing a prioritization challenge to Google.