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.

通过对罗马化僧伽罗社交媒体数据的利用，本研究提出了一个基于机器学习的框架，通过分析语言模式、情感和行为线索在全面的社交媒体帖子数据集中自动筛查抑郁症状。结果表明，提出的具有注意力层的神经网络在检测抑郁症状方面取得了 93.25％的显著准确度，超过了当前最先进的方法。这些发现强调了该方法在定位需要积极干预和支持的个体方面的功效。该研究对心理健康专业人士、政策制定者和社交媒体公司提供了有价值的见解。通过利用自然语言处理技术和机器学习算法，本研究为数字时代的心理健康筛查提供了一个有前途的路径。通过利用社交媒体数据的潜力，该框架引入了一种识别和援助抑郁风险个体的积极方法。总之，本研究在积极干预和支持心理健康系统方面做出了贡献，从而对领域内的研究和实际应用产生影响。