A semantic network analysis of doctoral well-being among Chinese PhD students

This study explores the emotional experiences of Chinese doctoral students using 13,435 user-generated posts from Zhihu, a leading Chinese knowledge-sharing platform. Collected between February and April 2025, the data were analyzed using a multi-layered sentiment analysis framework, including word frequency, polarity classification, topic modeling, and semantic network construction. We identify six key domains shaping emotional expression: supervisory support, academic community, social support, research motivation, time pressure, and self-perception. Emotional language frequently co-occurs with references to advisors, deadlines, lab environments, and institutional structures. Negative expressions center on time-related stress and institutional barriers, while positive emotions cluster around peer support and autonomy. Our contribution is twofold: we foreground Chinese doctoral students—an academically significant but understudied group—and validate established well-being mechanisms using large-scale text mining in a distinct cultural context. These findings promote a more inclusive and transferable understanding of doctoral well-being across diverse academic systems.