  Depression is a common mental illness that has to be detected and treated at
an early stage to avoid serious consequences. There are many methods and
modalities for detecting depression that involves physical examination of the
individual. However, diagnosing mental health using their social media data is
more effective as it avoids such physical examinations. Also, people express
their emotions well in social media, it is desirable to diagnose their mental
health using social media data. Though there are many existing systems that
detects 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 the
levels of depression as `not depressed', `moderately depressed' and `severely
depressed' from the social media postings. Traditional learning algorithms were
employed on this data set and an empirical analysis was presented in this
paper. Data augmentation technique was applied to overcome the data imbalance.
Among the several variations that are implemented, the model with Word2Vec
vectorizer and Random Forest classifier on augmented data outperforms the other
variations with a score of 0.877 for both accuracy and F1 measure.
