An Efficient Framework for Suicide Risk Detection Integrating Linguistic and Emotional Features Using Graph Neural Network

A summary of the contributions of our study is as follows:
1. A new graph-based framework leveraging social media data for suicide risk detection is proposed.
2. The integration of semantic (GloVe), syntactic (dependency parsing), and emotional (SenticNet) features in a single graph structure is proposed, correlating different perspectives of the data.
3. Here, we demonstrate the creation of more context-sensitive and interpretable models that would outperform certain recent ML, DL, and LLM approaches.
4. The performance of both GraphSAGE and HGNN was significantly improved when the dataset size was increased from 4K to 8K. GraphSAGE kept its reliability high, while HGNN achieved consistent best scores due to its capacity to scale and hence improve the accuracy, recall, and robustness with larger data.