Construction of Depression Recognition Model Based on Multi-Feature Fusion

doi:10.3969/j.issn.1006-2475.2025.03.001

Abstract

Abstract: In recent years， depression has become the primary problem of global mental health burden. In order to identify it， this paper proposes a depression recognition model combining BERT， BiLSTM and ConvNeXt. Firstly， the BERT model is used to generate feature vectors with rich semantics. Secondly， the BiLSTM， and ConvNeXt model is used to obtain the context information and the local features of the text， respectively. Thirdly， to alleviate the loss of semantic information in the feature extraction process， the context and local learned by BiLSTM and ConvNeXt models are fused through residual connections. Finally， depression is recognized according to the fused feature information. The experimental results show that the proposed model improves the accuracy， recall and F1 value compared with other deep learning models， which can effectively extract the depression features of the text and improve the accuracy of depression recognition.

Key words: , depression； BERT； BiLSTM； ConvNeXt； recognition

CLC Number:

HOU Menghan, WEI Changfa. Construction of Depression Recognition Model Based on Multi-Feature Fusion[J]. Computer and Modernization, 2025, 0(03): 1-5.

References

［1］蔡小溪，龚英龙，周志焕. 抑郁症对认知功能损害及中医药干预作用［J］. 辽宁中医药大学学报， 2019，21（10）：99-102.
［2］ World Health Organization. Depression and other common mental disorders： Global health estimates［R］. Geneva： World Health Organization， 2017.
［3］抑郁研究所.《2022年国民抑郁症蓝皮书》［EB/OL］. （2022-10-08）［2024-05-20］. https：//zhuanlan.zhihu.com/
p/571432462.
［4］ CACHEDA F， FERNANDEZ D， NOVOA F J， et al. Early detection of depression： Social network analysis and random forest techniques［J］. Journal of Medical Internet Research， 2019，21（6）. DOI： 10.2196/12554.
［5］ ALSAGRI H S， YKHLEF M. Machine learning-based approach for depression detection in Twitter using content and activity features［J］. IEICE Transactions on Information and Systems， 2020，103（8）：1825-1832.
［6］刘定平，张雪燕. 基于社交网络信息的用户抑郁症倾向识别［J］. 统计理论与实践， 2021（9）：14-21.
［7］ CHIONG R， BUDHI G S， DHAKAL S， et al. A textual-based featuring approach for depression detection using machine learning classifiers and social media texts［J］. Computers in Biology and Medicine， 2021，135. DOI： 10.1016/j.compbiomed.2021.104499.
［8］ OH J， LEE T， CHUNG E S， et al. Development of depression detection algorithm using text scripts of routine psychiatric interview［J］. Frontiers in Psychiatry， 2024，14. DOI： 10.3389/fpsyt.2023.1256571.
［9］ DING Y， CHEN X M， FU Q M， et al. A depression recognition method for college students using deep integrated support vector algorithm［J］. IEEE Access， 2020，8：75616
-75629.
［10］ MUSLEH D A， ALKHALES T A， ALMAKKI R A， et al. Twitter Arabic sentiment analysis to detect depression using machine learning［J］. Computers， Materials & Continua， 2022，71（2）：3463-3477.
［11］ AMANAT A， RIZWAN M， JAVED A R， et al. Deep learning for depression detection from textual data［J］. Electronics， 2022，11（5）：2079-9292.
［12］安正一. 基于多信息融合的社交媒体抑郁障碍检测研究［D］. 兰州：兰州大学， 2023.
［13］ CUI B， WANG J， LIN H， et al. Emotion-based reinforcement attention network for depression detection on social media： Algorithm development and validation［J］. JMIR Medical Informatics， 2022，10（8）：e37818.
［14］ SHI Y， TIAN Y， TONG C W， et al. Detect depression from social networks with sentiment knowledge sharing［C］// Proceedings of Chinese National Conference on Social Media Processing. Springer， 2023：133-146.
［15］ TIWARI S S， PANDEY R， DEEPAK A， et al. An ensemble approach to detect depression from social media platform： E-CLS［J］. Multimedia Tools and Applications， 2024，83：71001-71033.
［16］ PRABHAKAR K， KAVITHA V. An evolutionary approach for depression detection from Twitter big data using a novel deep learning model with attention based feature learning mechanism［J］. Automatika， 2024，65（2）：441-453.
［17］聂卉，吴晓燕. 基于在线医疗咨询文本的抑郁症症状短语的自动识别［J］. 现代情报， 2023，43（9）：63-73.
［18］ LIU Y. Depression detection via a Chinese social media platform： A novel causal relation-aware deep learning approach［J］. The Journal of Supercomputing， 2023，80（8）：1-30.
［19］ DEVLIN J， CHANG M W， LEE K， et al. BERT： Pre-training of deep bidirectional transformers for language understanding［C］// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics： Human Language Technologies. ACL， 2019：4171-4186.
［20］游嘉靖，何月顺，何璘琳，等. 基于AHP-CNN的加密流量分类方法［J］. 计算机与现代化， 2024（4）：83-87.
［21］田爱宝，魏娇娇，肖军弼. 基于Transformer的网络流量预测研究［J］. 信息技术， 2024（4）：156-160.
［22］隗昊，刁宏悦，孔亮宸，等. 东北亚舆情文本细粒度命名实体识别方法研究［J］. 计算机工程， 2024，50（5）：354-362.
［23］ HOCHREITER S， SCHMIDHUBER J. Long short-term memory［J］. Neural Computation， 1997，9：1735-1780.
［24］肖梦思，吴建斌，涂雅蒙，等. 基于I-ConvNeXt的GAN生成人脸图像鉴别方法［J］. 计算机与现代化， 2024（4）：38-42.
［25］ LIU Z. MAO H Z， WU C Y， et al. A ConvNet for the 2020s［C］// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2022：11966-11976.
［26］马晓，董天亮，钟闻宇，等. 基于改进ConvNeXt的大豆叶片病害分类研究［J］. 大豆科学， 2023，42（6）：733-741.
［27］ WANG H L， WU Z Z， LANG J Y. 派特心理：心理咨询问答语料库［EB/OL］. （2020-04-22）［2024-05-20］. https：
//github.com/chatopera/efaqa-corpus-zh.

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