A Decade of Music Emotion Computing: A Bibliometric Analysis of Trends, Interdisciplinary Collaboration, and Applications

This study systematically reviews the evolution of Music Emotion Computing (MEC) over the past decade, focusing on its two core branches: Music Emotion Recognition (MER) and Music Sentiment Analysis (MSA). Through a comprehensive bibliometric analysis, the research aims to uncover emerging trends, interdisciplinary and cross-regional collaboration patterns, and key application areas within this field. Using data collected from the Web of Science Core Collection (WoSCC), we conducted a comprehensive bibliometric analysis to map global academic output, highlighting influential studies, leading authors, and primary collaborative networks in MEC. Results indicate that research in MEC has exhibited significant growth over the last ten years, especially with heightened interest in applications such as multimodal emotion analysis and personalized music recommendation systems. MEC research demonstrates a high degree of interdisciplinary integration, with contributions from computer science, psychology, and neuroscience jointly driving advances in the field. Cross-regional collaboration analysis shows that Asia, Europe, and North America are the primary research hubs, characterized by extensive intercontinental partnerships. Current trends reveal a strong focus on multimodal MEC and deep learning-based methods combining audio, text, video as well as biosignals, suggesting future potential for MEC in areas like multimodal interaction, intelligent emotional feedback, and real-world applications, including mental health and music creation. Additionally, the study identifies challenges facing MEC applications, such as technical hurdles in multimodal data fusion, cultural variations in emotional perception, and concerns surrounding data privacy and ethics. Based on these findings, future research should further explore integration across diverse data sources, enhance the interpretability and generalizability of emotion recognition models, and innovate methods for cross-cultural emotion computing. This study provides a panoramic perspective for scholars in the MEC field and offers strategic recommendations for future research.