  The mental disorder of online users is determined using social media posts.
The major challenge in this domain is to avail the ethical clearance for using
the user generated text on social media platforms. Academic re searchers
identified the problem of insufficient and unlabeled data for mental health
classification. To handle this issue, we have studied the effect of data
augmentation techniques on domain specific user generated text for mental
health classification. Among the existing well established data augmentation
techniques, we have identified Easy Data Augmentation (EDA), conditional BERT,
and Back Translation (BT) as the potential techniques for generating additional
text to improve the performance of classifiers. Further, three different
classifiers Random Forest (RF), Support Vector Machine (SVM) and Logistic
Regression (LR) are employed for analyzing the impact of data augmentation on
two publicly available social media datasets. The experiments mental results
show significant improvements in classifiers performance when trained on the
augmented data.
