Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and global semantic features. In this paper, we propose a transformer-based model named TransformerRNN, which can effectively extract contextual and long-term dependency information by using a transformer encoder and a Bi-directional Long Short-Term Memory (BiLSTM) structure. We evaluate our model with baseline approaches on a dataset collected from online sources (including 659 suicide notes, 431 last statements, and 2000 neutral posts). Our proposed TransformerRNN achieves 95.0%, 94.9% and 94.9% performance in P, R and F1-score metrics respectively and therefore outperforms comparable machine learning and state-of-the-art deep learning models. The proposed model is effective for classifying suicide notes, which in turn, may help to develop suicide prevention technologies for social media.

自杀是世界范围内导致死亡的主要原因之一。与此同时，社交媒体的广泛使用导致越来越多的人在网上发布遗书。因此，设计一个可以帮助在线检测遗书的学习模型非常重要。然而，当前的方法不能同时捕获局部和全局语义特征。在本文中，我们提出了一种名为 TransformerRNN 的基于转换器的模型，该模型可以通过使用转换器编码器和双向长短期记忆 (BiLSTM) 结构有效地提取上下文和长期依赖信息。我们使用从在线来源收集的数据集（包括 659 份遗书、431 份最后声明和 2000 条中立帖子）使用基线方法评估我们的模型。我们提出的 TransformerRNN 达到了 95.0%、94.9% 和 94。P、R 和 F1 分数指标的性能分别为 9%，因此优于同类机器学习和最先进的深度学习模型。所提出的模型对于对遗嘱进行分类是有效的，这反过来可能有助于为社交媒体开发自杀预防技术。