TOUR: Dynamic Topic and Sentiment Analysis of User Reviews for Assisting App Release

TL;DR

TOUR is an automated tool that dynamically analyzes user reviews to identify emerging issues, sentiment trends, and prioritize reviews, aiding developers in app improvement across different versions.

Contribution

This paper introduces TOUR, a novel system combining online topic modeling and sentiment analysis for real-time review monitoring and issue detection in app development.

Findings

Tour effectively detects emerging issues over app versions.

Developers find TOUR's recommendations practically useful.

Tour accurately predicts user sentiment towards features.

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.