  Social media is one of the most highly sought resources for analyzing
characteristics of the language by its users. In particular, many researchers
utilized various linguistic features of mental health problems from social
media. However, existing approaches to detecting mental disorders face critical
challenges, such as the scarcity of high-quality data or the trade-off between
addressing the complexity of models and presenting interpretable results
grounded in expert domain knowledge. To address these challenges, we design a
simple but flexible model that preserves domain-based interpretability. We
propose a novel approach that captures the semantic meanings directly from the
text and compares them to symptom-related descriptions. Experimental results
demonstrate that our model outperforms relevant baselines on various mental
disorder detection tasks. Our detailed analysis shows that the proposed model
is effective at leveraging domain knowledge, transferable to other mental
disorders, and providing interpretable detection results.
