Abstract:
"Depression is one of the most serious medical conditions in the world that affects millions of people worldwide. In recent years, social media act as a popular platform for individuals to express thoughts and emotions they feel. The language used in these posts, particularly mix-coded text patterns in Sinhala and English, can provide valuable insights into an individual's mental state.
The goal of this research project is to create ""DepFlow,"" a technology that can detect depressive messages on social media using mix-coded text patterns in Sinhala and English. The tool will use a mix of natural language processing techniques and machine learning algorithms to evaluate the language used in social media posts. The study will begin by collecting a dataset of depressive and non-depressive social media posts in both Sinhala and English mix codes. This dataset will be used to train DepFlow's machine learning models. The technology will next be tested against a second dataset of social media posts to see how accurate it is at recognizing depressive messages.
DepFlow's development has the potential to provide a valuable tool for identifying those at risk of depression. Mental health practitioners may be able to intervene early and provide help to persons who are struggling with their mental health by examining their social media posts. Furthermore, the instrument can be used to track individuals' mental health over time, offering insights into the effectiveness of mental health therapies."
