Domain Knowledge Enhanced Text Mining for Identifying Mental Disorder Patterns

Mental health disorders may cause severe consequences for countries’ economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert’s knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.

I. Introduction

The mental health of individuals and communities is a pressing challenge in the world, nowadays. Since COVID-19 pandemic outbreak in 2019, most governments have been preoccupied with handling and combating the epidemic. COVID-19 has impacted our lifestyles and workplaces. These changes can cause us to feel frustrated, stressed, and anxious, which may seriously affect our mental health. The prevalence of Covid-19 has been significantly controlled and lowered as a result of the global success in vaccine development and mass inoculation. But now, for most governments, a key concern is harnessing the impacts of years of virus exposure (e.g., psychological and economic problems), as well as offering proper support to deal with these devastating effects and improve the situation.