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Why people hate your app: making sense of user feedback in a mobile app store

Authors:

Christos Faloutsos,

Norman SadehAuthors Info & Claims

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1276 - 1284

https://doi.org/10.1145/2487575.2488202

Published: 11 August 2013 Publication History

Abstract

User review is a crucial component of open mobile app markets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose Wiscom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users' reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users' major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.

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Cited By

Assi MHassan SZou Y(2026)LLM-Cure: LLM-Based Competitor User Review Analysis for Feature EnhancementACM Transactions on Software Engineering and Methodology10.1145/374464435:4(1-25)Online publication date: 11-Mar-2026
https://dl.acm.org/doi/10.1145/3744644
Amanullah Ullah SChohan RNawaz Khan M(2026)Understanding Perceived Quality of Mobile Apps Using DistilBERT ModelIEEE Access10.1109/ACCESS.2025.364827914(3560-3576)Online publication date: 2026
https://doi.org/10.1109/ACCESS.2025.3648279
Groen EDalpiaz Fvan Vliet MWinter BDoerr JBrinkkemper S(2025)Classification of quality characteristics in online user feedback using linguistic analysis, crowdsourcing and LLMsJournal of Systems and Software10.1016/j.jss.2025.112533230:COnline publication date: 18-Dec-2025
https://dl.acm.org/doi/10.1016/j.jss.2025.112533
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