Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2021

Abstract

App reviews deliver user opinions and emerging issues (e.g., new bugs) about the app releases. Due to the dynamic nature of app reviews, topics and sentiment of the reviews would change along with app release versions. Although several studies have focused on summarizing user opinions by analyzing user sentiment towards app features, no practical tool is released. The large quantity of reviews and noise words also necessitates an automated tool for monitoring user reviews. In this paper, we introduce TOUR for dynamic TOpic and sentiment analysis of User Reviews. TOUR is able to (i) detect and summarize emerging app issues over app versions, (ii) identify user sentiment towards app features, and (iii) prioritize important user reviews for facilitating developers’ examination. The core techniques of TOUR include the online topic modeling approach and sentiment prediction strategy. TOUR provides entries for developers to customize the hyper-parameters and the results are presented in an interactive way. We evaluate TOUR by conducting a developer survey that involves 15 developers, and all of them confirm the practical usefulness of the recommended feature changes by TOUR.

Keywords

App review, review topic, sentiment analysis

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of WWW'21: The Web Conference 2021, Ljubljana, Slovenia, April 19-23

First Page

708

Last Page

712

Identifier

10.1145/3442442.3458612

Publisher

ACM

City or Country

Ljubljana, Slovenia

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