Utilizing transformer for emotional understanding on Chinese mental-health dataset

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dc.contributor.authorDu, Mingyu
dc.contributor.supervisorDong, Xiaodai
dc.date.accessioned2025-06-02T15:32:10Z
dc.date.available2025-06-02T15:32:10Z
dc.date.issued2025
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Engineering MEng
dc.description.abstractThe rapid development of large language models has demonstrated successful performance in various areas. In terms of mental health, large language models exhibit the capability to understand emotional feeling to some extent. However, research in the mental health field requires a broad range of interdisciplinary knowledge and is often constrained by limited resources. This project focuses on the analysis of sentiment in conversational texts using large language models and investigating the model performances. By comparing 8 different open source models, the project demonstrates the outstanding performance of hfl/chinese-roberta-wwm-ext in emotional understanding using the mental health dataset released by Tongji University.
dc.description.scholarlevelGraduate
dc.identifier.urihttps://hdl.handle.net/1828/22325
dc.language.isoen
dc.rightsAvailable to the World Wide Web
dc.subjecttransformer
dc.titleUtilizing transformer for emotional understanding on Chinese mental-health dataset
dc.typeproject

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