  This work explores the utilization of Romanized Sinhala social media data to
identify individuals at risk of depression. A machine learning-based framework
is presented for the automatic screening of depression symptoms by analyzing
language patterns, sentiment, and behavioural cues within a comprehensive
dataset of social media posts. The research has been carried out to compare the
suitability of Neural Networks over the classical machine learning techniques.
The proposed Neural Network with an attention layer which is capable of
handling long sequence data, attains a remarkable accuracy of 93.25% in
detecting depression symptoms, surpassing current state-of-the-art methods.
These findings underscore the efficacy of this approach in pinpointing
individuals in need of proactive interventions and support. Mental health
professionals, policymakers, and social media companies can gain valuable
insights through the proposed model. Leveraging natural language processing
techniques and machine learning algorithms, this work offers a promising
pathway for mental health screening in the digital era. By harnessing the
potential of social media data, the framework introduces a proactive method for
recognizing and assisting individuals at risk of depression. In conclusion,
this research contributes to the advancement of proactive interventions and
support systems for mental health, thereby influencing both research and
practical applications in the field.
