Depression tendency detection method based on multi-feature fusion of BERT word embeddings

Lin Han; Xu Zhang

doi:10.1117/12.3011971

7 December 2023 Depression tendency detection method based on multi-feature fusion of BERT word embeddings

Lin Han, Xu Zhang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294124 (2023) https://doi.org/10.1117/12.3011971
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Depression is a common psychological disorder. In order to detect and identify the tendency of depression in text as early and accurately as possible, a method for depression tendency detection based on multi-feature fusion of BERT word embeddings is proposed. An emotion classification model is employed for vector representation of depressive text, extracting vectors that fuse multi-level semantic features and emotional features. The TextCNN and BiLSTM-Attention modules are established to extract local and contextual features of depressive text. Finally, multiple features are fused to determine the tendency of depression in the text. Experimental results demonstrate that the proposed method, which fuses multiple features of depressive text, achieves an improvement of approximately 5% in various evaluation metrics compared to traditional deep learning methods.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lin Han and Xu Zhang "Depression tendency detection method based on multi-feature fusion of BERT word embeddings", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294124 (7 December 2023); https://doi.org/10.1117/12.3011971

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