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Mental health disorders may cause severe consequences on all the countries'economies and health. For example, the impacts of the COVID-19 pandemic, suchas isolation and travel ban, can make us feel depressed. Identifying earlysigns of mental health disorders is vital. For example, depression may increasean individual's risk of suicide. The state-of-the-art research in identifyingmental disorder patterns from textual data, uses hand-labelled training sets,especially when a domain expert's knowledge is required to analyse varioussymptoms. This task could be time-consuming and expensive. To address thischallenge, in this paper, we study and analyse the various clinical andnon-clinical approaches to identifying mental health disorders. We leverage thedomain knowledge and expertise in cognitive science to build a domain-specificKnowledge Base (KB) for the mental health disorder concepts and patterns. Wepresent a weaker form of supervision by facilitating the generating of trainingdata from a domain-specific Knowledge Base (KB). We adopt a typical scenariofor analysing social media to identify major depressive disorder symptoms fromthe textual content generated by social users. We use this scenario to evaluatehow our knowledge-based approach significantly improves the quality of results.

Towards Knowledge-based Mining of Mental Disorder Patterns from Textual Data