Citation

Zhou TH, Hu GL, Wang L. Int. J. Environ. Res. Public Health 2019; 16(6): e16060953.

Affiliation

Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132000, China. smile2867ling@neepu.edu.cn.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph16060953

PMID

30884824

Abstract

The Institute for Health Metrics and Evaluation (IHME) has stated that over 1.1 billion people suffered from mental disorders globally in 2016, and the burden of mental disorders has continued to grow with impacts on social development. Despite the implementation of strategies for promotion and prevention in mental health WHO's Comprehensive Mental Health Action Plan 2013⁻2020, the difficulty of diagnosis of mental disorders makes the objective "To provide comprehensive, integrated, and responsive mental health and social care services in community-based settings" hard to carry out. This paper presents a mental-disorder-aided diagnosis model (MDAD) to quantify the multipolarity sentiment affect intensity of users' short texts in social networks in order to analyze the 11-dimensional sentiment distribution. We searched the five mental disorder topics and collected data based on Twitter hashtag. Through sentiment distribution similarity calculations and Stochastic Gradient Descent (SGD), people with a high probability of suffering from mental disorder can be detected in real time. In particular, mental health warnings can be made in time for users with an obvious emotional tendency in their tweets. In the experiments, we make a comprehensive evaluation of MDAD by five common adult mental disorders: depressive disorder, anxiety disorder, obsessive-compulsive disorder (OCD), bipolar disorder, and panic disorder. Our proposed model can effectively diagnose common mental disorders by sentiment multipolarity analysis, providing strong support for the prevention and diagnosis of mental disorders.

Language: en

Keywords

emotion perception; machine learning; psychological disorder; sentiments distribution; social network