Causal Categorization of Mental Health Posts using Transformers
This work addresses the challenge of causal analysis in mental health detection on social media, which is incremental as it applies existing transformer methods to a specific domain problem.
The paper tackled the problem of causal categorization in mental health posts from social media, addressing inefficiencies of learning-based methods due to limited training samples, and demonstrated that using transformer models with pre-trained transfer learning on the 'CAMS' dataset improved accuracy.
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.