Publication Type

Conference Proceeding Article

Version

submittedVersion

Publication Date

6-2014

Abstract

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.

Keywords

user feedback, mobile application, user reviews, data mining

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

ICSE 2014: 36th International Conference on Software Engineering: Proceedings: May 31-June 7, Hyderabad, India

First Page

767

Last Page

778

ISBN

9781450327565

Identifier

10.1145/2568225.2568263

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/2568225.2568263

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