From Emotional Expression to Group Classification: Construction and Characteristic Study of User Emotional Portraits in Online Music Communities

Online music communities (OMCs) have emerged as a pivotal platform for users’ emotional communication and mood regulation, yet the distinct emotional expression groups and their characteristic differences within this context remain unclear. This study develops a four-dimensional emotional portrait label system for OMCs users, covering emotion, topic, basic information characteristics, and community interaction behavior. Taking China’s NetEase Cloud Music community in China as a case study,sentiment analysis, topic analysis, quantitative analysis, and cluster analysis are employed for data processing and analysis. The results reveal five types of emotionally distinct users in OMCs, who exhibit sporadic community participation and are concentrated among digital natives in developed regions with fast-paced, high-stress lives, while also revealing the emergence of China’s “Demotivation Culture”. By characterizing different emotional groups, this study expands the theoretical framework of user portrait and offers insightful guidance for optimizing personalized services and enhancing user stickiness in OMCs.