Postgraduate psychological stress detection from social media using BERT-Fused model.

ABSTRACT: Postgraduate students face various academic, personal, and social stressors that increase their risk of anxiety, depression, and suicide. Identifying cost-effective methods of detecting and intervening before stress turns into severe problems is crucial. However, existing stress detection methods typically rely on psychological scales or devices, which can be complex and expensive. Therefore, we propose a BERT-fused model for rapidly and automatically detecting postgraduate students' psychological stress via social media. First, we construct an improved BERT-LDA feature extraction algorithm to extract group stress features from large-scale and complex social media data. Then, we integrate the BiLSTM-CRF named entity recognition model to construct a multi-dimensional psychological stress pr

SUBMITTER: Zhuang M 

PROVIDER: S-EPMC11527284 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature