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

Causal Categorization of Mental Health Posts using Transformers