Article: Student mental health assessment based on social sentiment analysis and multi-branch neural networks Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.45 pp.23 - 41 Abstract: Student mental health issues are becoming increasingly severe, yet traditional scale assessments suffer from limitations such as high subjectivity and delayed feedback. To address this challenge, this paper proposes an intelligent evaluation framework multi-branch adaptive social-emotional fusion network that integrates social-emotional analysis with multi-branch neural networks. This framework continuously and seamlessly integrates multidimensional digital footprints generated by students in campus life (e.g., text, voice, behavioural patterns) to enable dynamic psychological risk assessment. In this work, digital footprints refer to passively collected multimodal data from students' daily digital activities, including text messages, voice recordings, smartphone sensor logs, and social interaction records. Experiments on the public studentlife dataset demonstrate that the proposed method achieves a key evaluation metric area under the receiver operating characteristic curve of 0.927, surpassing mainstream unimodal models by over 3.2%. It also achieves an overall accuracy of 89.7% and passes statistical significance tests. This confirms the effectiveness and feasibility of utilising multi-source socio-emotional signals for early, objective intervention. Inderscience Publishers - linking academia, business and industry through research

Title: Student mental health assessment based on social sentiment analysis and multi-branch neural networks

Authors: Yue Zheng

Addresses: Business School, Huanghe Science and Technology University, Zhengzhou, 450063, China

Abstract: Student mental health issues are becoming increasingly severe, yet traditional scale assessments suffer from limitations such as high subjectivity and delayed feedback. To address this challenge, this paper proposes an intelligent evaluation framework multi-branch adaptive social-emotional fusion network that integrates social-emotional analysis with multi-branch neural networks. This framework continuously and seamlessly integrates multidimensional digital footprints generated by students in campus life (e.g., text, voice, behavioural patterns) to enable dynamic psychological risk assessment. In this work, digital footprints refer to passively collected multimodal data from students' daily digital activities, including text messages, voice recordings, smartphone sensor logs, and social interaction records. Experiments on the public studentlife dataset demonstrate that the proposed method achieves a key evaluation metric area under the receiver operating characteristic curve of 0.927, surpassing mainstream unimodal models by over 3.2%. It also achieves an overall accuracy of 89.7% and passes statistical significance tests. This confirms the effectiveness and feasibility of utilising multi-source socio-emotional signals for early, objective intervention.

Keywords: student mental health; social-emotional analysis; multi-branch neural networks; intelligent assessment; digital footprint.

DOI: 10.1504/IJICT.2026.153516

International Journal of Information and Communication Technology, 2026 Vol.27 No.45, pp.23 - 41

Received: 15 Dec 2025
Accepted: 20 Jan 2026
Published online: 12 May 2026 *

Title: Student mental health assessment based on social sentiment analysis and multi-branch neural networks

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