  Mental health disorders may cause severe consequences on all the countries'
economies and health. For example, the impacts of the COVID-19 pandemic, such
as isolation and travel ban, can make us feel depressed. Identifying early
signs of mental health disorders is vital. For example, depression may increase
an individual's risk of suicide. The state-of-the-art research in identifying
mental disorder patterns from textual data, uses hand-labelled training sets,
especially when a domain expert's knowledge is required to analyse various
symptoms. This task could be time-consuming and expensive. To address this
challenge, in this paper, we study and analyse 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 (KB) for the mental health disorder concepts and patterns. We
present a weaker form of supervision by facilitating the generating of training
data from a domain-specific Knowledge Base (KB). We adopt a typical scenario
for analysing social media to identify major depressive disorder symptoms from
the textual content generated by social users. We use this scenario to evaluate
how our knowledge-based approach significantly improves the quality of results.
