Thu 27 Jun 2024 15:45 - 16:45 at V101 - Most Influential Paper + Panel

App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.

Thu 27 Jun

Displayed time zone: (UTC) Coordinated Universal Time change

15:45 - 17:45
Most Influential Paper + PanelPanels / Most Influential Paper at V101
15:45
60m
Talk
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews
Most Influential Paper
A: Emitzá Guzmán Vrije Universiteit Amsterdam, A: Walid Maalej University of Hamburg
Link to publication DOI
16:45
60m
Panel
Panel: Requirements Engineering in the Era of Intelligent Cyber-Physical Systems
Panels
S: Jane Cleland-Huang University of Notre Dame, P: Bashar Nuseibeh The Open University, UK, P: Jan-Philipp Steghöfer XITASO GmbH IT & Software Solutions, P: Nelly Bencomo Durham University, P: Chattie McBotface