CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
Proceedings of the Thirteenth International Conference on Language Resources and Evaluation (LREC 2022)
Abstract
The social NLP researchers and mental health practitioners have witnessed exponential growth in the field of mental health detection and analysis on social media. It has become important to identify the reason behind mental illness. In this context, we introduce a new dataset for Causal Analysis of Mental health in Social media posts (CAMS). We first introduce the annotation schema for this task of causal analysis. The causal analysis comprises of two types of annotations, viz, causal interpretation and causal categorization. We show the efficacy of our scheme in two ways: (i) crawling and annotating 3155 Reddit data and (ii) re-annotate the publicly available SDCNL dataset of 1896 instances for interpretable causal analysis. We further combine them as CAMS dataset and make it available along with the other source codes https://anonymous.4open.science/r/CAMS1/. Our experimental results show that the hybrid CNN-LSTM model gives the best performance over CAMS dataset.