Enhancing User Trust and Interpretability in AI-Driven Feature Request Detection for Mobile App Reviews: An Explainable Approach

Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced e...

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Veröffentlicht in:	IEEE access Jg. 12; S. 114023 - 114045
Hauptverfasser:	Gambo, Ishaya, Massenon, Rhodes, Lin, Chia-Chen, Ogundokun, Roseline Oluwaseun, Agarwal, Saurabh, Pak, Wooguil
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Analytical models Annotations Applications programs Artificial intelligence Artificial neural networks Explainable AI Explainable artificial intelligence Feature extraction feature request detection Interactive systems Machine learning machine learning interpretability mobile app development Mobile applications Mobile computing Reviews Sentiment analysis Software engineering software requirements Support vector machines Trust management Trustworthiness user trust
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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