DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method

Depression is linked to the development of dementia. Cognitive functions such as thinking and remembering generally deteriorate in dementia patients. Social media usage has been increased among the people in recent days. The technology advancements help the community to express their views publicly. Analysing the signs of depression from texts has become an important area of research now, as it helps to identify this kind of mental disorders among the people from their social media posts. As part of the shared task on detecting signs of depression from social media text, a dataset has been provided by the organizers (Sampath et al.). We applied different machine learning techniques such as Support Vector Machine, Random Forest and XGBoost classifier to classify the signs of depression. Experimental results revealed that, the XGBoost model outperformed other models with the highest classification accuracy of 0.61% and an Macro F-1 score of 0.54.