  With recent developments in digitization of clinical psychology, NLP research
community has revolutionized the field of mental health detection on social
media. Existing research in mental health analysis revolves around the
cross-sectional studies to classify users' intent on social media. For in-depth
analysis, we investigate existing classifiers to solve the problem of causal
categorization which suggests the inefficiency of learning based methods due to
limited training samples. To handle this challenge, we use transformer models
and demonstrate the efficacy of a pre-trained transfer learning on "CAMS"
dataset. The experimental result improves the accuracy and depicts the
importance of identifying cause-and-effect relationships in the underlying
text.
