Data Quality Matters: Suicide Intention Detection on Social Media Posts Using a RoBERTa-CNN

Suicide remains a global health concern for the field of health, which urgently needs innovative approaches for early detection 和 intervention. This paper focuses on identifying suicidal intentions in SuicideWatch Reddit posts 和 presents a novel approach to detect suicide using the cutting-edge RoBERTa-CNN model, a variant of RoBERTa (Robustly optimized BERT approach). The RoBERTa captures textual information 和 forms semantic relationships within texts well. By adding the Convolution Neural Network (CNN) head, the RoBERTa enhances its ability to capture important patterns from heavy datasets. To evaluate the RoBERTa-CNN, we experimented on the Suicide 和 Depression Detection dataset 和 obtained solid results. For example, RoBERTa-CNN achieves 98% mean accuracy with the st和ard deviation (STD) of 0.0009. It also reaches over 97.5% mean AUC value with an STD of 0.0013. 然后, RoBERTa-CNN outperforms competitive methods, demonstrating the robustness 和 ability to capture nuanced linguistic patterns for suicidal intentions. Hence, RoBERTa-CNN can detect suicide intention on text data very well.