用混合模型提升社交媒体自残念头检测准确率与可解释性
Enhanced Suicidal Ideation Detection from Social Media Using a CNN-BiLSTM Hybrid Model
- 结合CNN、BiLSTM和注意力机制捕捉文本语义与上下文
- 准确率从92.81%提升至94.29%，关键特征指向心理健康相关词汇
- 引入SHAP分析增强结果可信度，适合心理监测系统开发者
自残念头检测对预防自杀这一全球主要死因至关重要。许多人在社交媒体上表达此类想法，为利用先进机器学习技术进行早期发现提供了重要机会。本文提出一种融合卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM）的混合框架，并引入注意力机制以提升社交文本中自残念头的识别效果。为增强模型预测的可解释性，采用可解释人工智能（XAI）方法，重点使用SHapley Additive exPlanations（SHAP）。初始模型准确率达92.81%；通过微调与早停策略优化后，准确率提升至94.29%。SHAP分析揭示了影响预测的关键特征，如与心理健康困扰相关的词汇。该透明性提升模型可信度，助力心理健康专业人员理解并信任预测结果。本研究展示了融合强大学习方法与可解释性的潜力，推动心理健康监测系统的可靠发展。
原文摘要 · Abstract (English)
Suicidal ideation detection is crucial for preventing suicides, a leading cause of death worldwide. Many individuals express suicidal thoughts on social media, offering a vital opportunity for early detection through advanced machine learning techniques. The identification of suicidal ideation in social media text is improved by utilising a hybrid framework that integrates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), enhanced with an attention mechanism. To enhance the interpretability of the model's predictions, Explainable AI (XAI) methods are applied, with a particular focus on SHapley Additive exPlanations (SHAP), are incorporated. At first, the model managed to reach an accuracy of 92.81%. By applying fine-tuning and early stopping techniques, the accuracy improved to 94.29%. The SHAP analysis revealed key features influencing the model's predictions, such as terms related to mental health struggles. This level of transparency boosts the model's credibility while helping mental health professionals understand and trust the predictions. This work highlights the potential for improving the accuracy and interpretability of detecting suicidal tendencies, making a valuable contribution to the progress of mental health monitoring systems. It emphasizes the significance of blending powerful machine learning methods with explainability to develop reliable and impactful mental health solutions.
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