Identifying emotional causes of mental disorders from social media for effective intervention

• Main Authors: Huangfu, L. (Author), Ji, Y. (Author), Liang, Y. (Author), Liu, L. (Author), Zeng, D.D (Author)
• Format: Article
• Language: English
• Published: Elsevier Ltd 2023
• Subjects: Causes analysis; Complex causality; Complex emotions; Contrastive learning; Diseases; Emotion-cause pair extraction; Large dataset; Long short-term memory; Mental disorders; Mental health; Mental illness; Simple++; Social media; Social networking (online)
• Online Access: View Fulltext in Publisher; View in Scopus

 Identifying the emotional causes of mental illnesses is key to effective intervention. Existing emotion-cause analysis approaches can effectively detect simple emotion-cause expressions where only one cause and one emotion exist. However, emotions may often result from multiple causes, implicitly or explicitly, with complex interactions among these causes. Moreover, the same causes may result in multiple emotions. How to model the complex interactions between multiple emotion spans and cause spans remains under-explored. To tackle this problem, a contrastive learning-based framework is presented to detect the complex emotion-cause pairs with the introduction of negative samples and positive samples. Additionally, we developed a large-scale emotion-cause dataset with complex emotion-cause instances based on subreddits associated with mental health. Our proposed approach was compared to prevailing CNN-based, LSTM-based, Transformer-based and GNN-based methods. Extensive experiments have been conducted and the quantifiable outcomes indicate that our proposed solution achieves competitive performance on simple emotion-cause pairs and significantly outperformed baseline methods in extracting complex emotion-cause pairs. Empirical studies further demonstrated that our proposed approach can be used to reveal the emotional causes of mental disorders for effective intervention. © 2023 The Author(s)