Data Quality Matters: Suicide Intention Detection on Social Media Posts
  Using a RoBERTa-CNN Model
Emily Lin Jian Sun Hsingyu Chen Mohammad H. Mahoor
Suicide remains a global health concern for the field of health, which
urgently needs innovative approaches for early detection and intervention. In
this paper, we focus on identifying suicidal intentions in SuicideWatch Reddit
posts and present a novel approach to suicide detection using the cutting-edge
RoBERTa-CNN model, a variant of RoBERTa (Robustly optimized BERT approach).
RoBERTa is used for various Natural Language Processing (NLP) tasks, including
text classification and sentiment analysis. The effectiveness of the RoBERTa
lies in its ability to capture textual information and form semantic
relationships within texts. By adding the Convolution Neural Network (CNN)
layer to the original model, the RoBERTa enhances its ability to capture
important patterns from heavy datasets. To evaluate the RoBERTa-CNN, we
experimented on the Suicide and Depression Detection dataset and obtained solid
results. For example, RoBERTa-CNN achieves 98% mean accuracy with the standard
deviation (STD) of 0.0009. It also reaches over 97.5% mean AUC value with an
STD of 0.0013. In the meanwhile, RoBERTa-CNN outperforms competitive methods,
demonstrating the robustness and ability to capture nuanced linguistic patterns
for suicidal intentions. Therefore, RoBERTa-CNN can detect suicide intention on
text data very well.