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Depression is a common mental illness that has to be detected and treated atan early stage to avoid serious consequences. There are many methods andmodalities for detecting depression that involves physical examination of theindividual. However, diagnosing mental health using their social media data ismore effective as it avoids such physical examinations. Also, people expresstheir emotions well in social media, it is desirable to diagnose their mentalhealth using social media data. Though there are many existing systems thatdetects mental illness of a person by analysing their social media data,detecting the level of depression is also important for further treatment.Thus, in this research, we developed a gold standard data set that detects thelevels of depression as `not depressed', `moderately depressed' and `severelydepressed' from the social media postings. Traditional learning algorithms wereemployed on this data set and an empirical analysis was presented in thispaper. Data augmentation technique was applied to overcome the data imbalance.Among the several variations that are implemented, the model with Word2Vecvectorizer and Random Forest classifier on augmented data outperforms the othervariations with a score of 0.877 for both accuracy and F1 measure.

Data set creation and empirical analysis for detecting signs of depression from social media postings