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.
Highlights
The World Health Organization (WHO) indicated that one in four people in the world will be affected by mental or neurological disorders at some point in their lives
We propose a mental disorder aided diagnosis model to analyze the probabilities of suffering from five common adult mental disorders, which is more conducive to monitoring the mental health of social network users
We studied the relationship between mental disorders and the multipolarity sentiment distribution of social network users to propose a mental disorder aided diagnosis model (MDAD) based on sentiment multipolarity analysis
Summary
The World Health Organization (WHO) indicated that one in four people in the world will be affected by mental or neurological disorders at some point in their lives. A study commissioned by the World Economic Forum (WEF) predicts that mental disorders will become the biggest health cost by 2030, with global costs rising to $6 trillion each year [4]. Plan 2013–2020 for the 20 years calls for the strengthening of “Information Systems, Evidence, and Research” [5,6], which requires new developments and improvements in global mental health monitoring capabilities. Public Health 2019, 16, 953; doi:10.3390/ijerph16060953 www.mdpi.com/journal/ijerph
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