Fudan Development Institute, Fudan University, Shanghai, P.R.China.
Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, P.R.China.
Biotechnol Genet Eng Rev. 2024 Nov;40(3):1821-1835. doi: 10.1080/02648725.2023.2196846. Epub 2023 Apr 7.
The COVID-19 pandemic has caused a series of effects on the mental health of college students, especially long-term home isolation or online learning, which has caused college students to have both academic pressure and employment pressure. How to accurately and effectively assess the mental health status of college students has become a research hotspot. Traditional methods based on questionnaires such as Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) are difficult to collect data and have poor evaluation accuracy. This paper analyzes the psychological state through text-images of multi-modal data with tensor fusion networks and constructs a mental health assessment model for college students. First, the validity of the model is verified through the MVSA (Multi-View Sentiment Analysis) dataset. Second, the psychological state of college students under the epidemic is analyzed using the collected text-images dataset. The results show that the TFN-MDA (Tensor Fusion Network-Multimodal Data Analysis) based mental health assessment model constructed in this paper can effectively assess the mental health status of college students, with an average accuracy of more than 70%.
新冠疫情对大学生的心理健康造成了一系列影响，尤其是长期的居家隔离或在线学习，使大学生既有学业压力又有就业压力。如何准确有效地评估大学生的心理健康状况已成为研究热点。传统的基于问卷的方法，如抑郁自评量表（SDS）和焦虑自评量表（SAS），难以收集数据且评估准确性较差。本文通过张量融合网络对多模态数据的文本-图像进行分析，构建了一种大学生心理健康评估模型。首先，通过 MVSA（多视角情感分析）数据集验证模型的有效性。其次，使用收集的文本-图像数据集分析疫情下大学生的心理状态。结果表明，本文构建的基于 TFN-MDA（张量融合网络-多模态数据分析）的心理健康评估模型能够有效评估大学生的心理健康状况，平均准确率超过 70%。
