The increasing prevalence of mental health disorders, particularly depression and anxiety, highlights the pressing need for effective early detection mechanisms. Existing approaches often focus on analyzing social media data or utilizing standardized tools like the PHQ-9 and GAD-7. Notably, prior studies on mental illness detection have demonstrated significant success, achieving accuracies of 91% using vector-space word embeddings and 98% when combined with lexicon-based features. However, these methods are limited by their reliance on single-source data, which may not capture the full complexity of mental health. This paper proposes a hybrid approach combining questionnaire analysis with social media data analysis.
