Description

Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatment..In this project, we argue that it is feasible to identify depression at an early stage by mining online social behaviours. Our approach, which is innovative to the practice of depression detection, does not rely on the extraction of numerous or complicated features to achieve accurate depression detection. Instead, Classifier, namely, Ada Boost and Decision Tree Classifier, which demonstrates a strong classification ability on two publicly accessible Twitter depression detection datasets. Finally, we comprehensively explore the influence factors for the model prediction, and the results manifest that our proposed frame- work is promising for identifying Twitter users with depression.