2026-04-14T06:59:02Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4472272026-01-21T09:51:02Zcom_2072_1033col_2072_452950

What about emotions? Guiding fine-grained emotion extraction from mobile app reviews Motger de la Encarnación, Joaquim Oriol Hilari, Marc Tiessler Aguirre, Max Franch Gutiérrez, Javier Marco Gómez, Jordi Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació Universitat Politècnica de Catalunya. Departament de Ciències de la Computació Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering Àrees temàtiques de la UPC::Informàtica::Enginyeria del software Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural Mobile apps App reviews Emotions Opinion mining Annotation Dataset Large language models Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classification in app reviews remains underexplored. Fine-grained emotion classification is thus needed to better understand users’ affective responses and support downstream tasks such as feature-emotion analysis, user-oriented release planning, and issue triaging. This paper addresses this gap by identifying and addressing the challenges and limitations in fine-grained emotion analysis in the context of app reviews. Our study adapts Plutchik’s emotion taxonomy to app reviews by developing a structured annotation framework and dataset. Through an iterative human annotation process, we define clear annotation guidelines and document key challenges in emotion classification. Additionally, we evaluate the feasibility of automating emotion annotation using large language models, assessing their cost-effectiveness and agreement with human-labelled data. Our findings reveal that while large language models significantly reduce manual effort and maintain substantial agreement with human annotators, full automation remains challenging due to the complexity of emotional interpretation. This work contributes to opinion mining in requirements engineering by providing structured guidelines, an annotated dataset, and insights for developing automated pipelines to capture the complexity of emotions in app reviews. This work has been supported by funding from the HIVEMIND project – Horizon Europe call HORIZON-CL4-2024- DIGITAL-EMERGING-01 under Grant Agreement Number 101189745. This paper has been funded by the Spanish Ministerio de Ciencia e Innovacióin under project/funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033. Peer Reviewed Postprint (author's final draft) 2025 Conference lecture Motger, Q. [et al.]. What about emotions? Guiding fine-grained emotion extraction from mobile app reviews. A: IEEE International Requirements Engineering Conference. «2025 IEEE 33rd International Requirements Engineering Conference, RE 2025: 1-5 September 2025, Valencia, Spain: proceedings». Institute of Electrical and Electronics Engineers (IEEE), 2025, p. 6-18. ISBN 979-8-3315-2413-5. DOI 10.1109/RE63999.2025.00012 . 979-8-3315-2413-5 https://hdl.handle.net/2117/447227 10.1109/RE63999.2025.00012 eng https://ieeexplore.ieee.org/document/11190331 info:eu-repo/grantAgreement/EC/HE/101189745/EU/Human-centred collaboratIVE MultI-ageNt framework for accelerating software Development and maintenance/HIVEMIND info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117191RB-I00/ES/DESARROLLO, OPERATIVA Y GOBERNANZA DE DATOS PARA SISTEMAS SOFTWARE BASADOS EN APRENDIZAJE AUTOMATICO/ Open Access 13 p. application/pdf Institute of Electrical and Electronics Engineers (IEEE)