论文 | 基于多模态数据的张量融合网络的疫情期间大学生心理健康评估模型
College students’ mental health evaluation model based on tensor fusion network with multimodal data during the COVID-19 pandemic
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发表期刊
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Biotechnology and Genetic Engineering Reviews
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作者简介
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01
朱清君 复旦发展研究院
02
熊建超 上海全景医学影像诊断中心
03
彭莉玲 上海全景医学影像诊断中心
简介
新冠肺炎疫情给大学生的心理健康带来了一系列的影响，尤其是在长期的居家隔离和在线学习的环境下，使得大学生既面临着学业压力，又承受着就业压力。因此，如何准确有效地评估大学生的心理健康状况成为最近几年的研究热点。
然而，传统的问卷调查例如抑郁自评量表（SDS）和焦虑自评量表（SAS）的数据收集较难，且在这种环境下的评估准确性较差。本文通过多模态数据的文本-图像与张量融合网络分析心理状态，构建了一种适用于大学生心理健康的评估模型。首先，通过MVSA（多视角情感分析）数据集验证了模型的有效性。其次，利用收集到的文本-图像数据集分析了疫情下大学生的心理状态。结果显示，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地评估大学生的心理健康状况，平均准确率超过70%。
ABSTRACT
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%.
KEYWORDS
Mental health，CNN，tensor fusion network，multimodal data
本文链接
https://www.tandfonline.com/doi/full/10.1080/02648725.2023.2196846
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