Striegl Julian, Richter Jordan Wenzel, Grossmann Leoni, Bråstad Björn, Gotthardt Marie, Rück Christian, Wallert John, Loitsch Claudia
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig), Technische Universität Dresden, Dresden, Saxony, Germany.
Chair of Human-Computer Interaction, Technische Universität Dresden, Dresden, Saxony, Germany.
PeerJ Comput Sci. 2024 Jun 24;10:e2104. doi: 10.7717/peerj-cs.2104. eCollection 2024.
Internet-based cognitive behavioral therapy (iCBT) offers a scalable, cost-effective, accessible, and low-threshold form of psychotherapy. Recent advancements explored the use of conversational agents such as chatbots and voice assistants to enhance the delivery of iCBT. These agents can deliver iCBT-based exercises, recognize and track emotional states, assess therapy progress, convey empathy, and potentially predict long-term therapy outcome. However, existing systems predominantly utilize categorical approaches for emotional modeling, which can oversimplify the complexity of human emotional states. To address this, we developed a transformer-based model for dimensional text-based emotion recognition, fine-tuned with a novel, comprehensive dimensional emotion dataset comprising 75,503 samples. This model significantly outperforms existing state-of-the-art models in detecting the dimensions of valence, arousal, and dominance, achieving a Pearson correlation coefficient of = 0.90, = 0.77, and = 0.64, respectively. Furthermore, a feasibility study involving 20 participants confirmed the model's technical effectiveness and its usability, acceptance, and empathic understanding in a conversational agent-based iCBT setting, marking a substantial improvement in personalized and effective therapy experiences.
基于互联网的认知行为疗法（iCBT）提供了一种可扩展、经济高效、易于获取且门槛较低的心理治疗形式。最近的进展探索了使用聊天机器人和语音助手等对话代理来加强iCBT的实施。这些代理可以提供基于iCBT的练习、识别和跟踪情绪状态、评估治疗进展、表达同理心，并有可能预测长期治疗结果。然而，现有系统主要采用分类方法进行情绪建模，这可能会过度简化人类情绪状态的复杂性。为了解决这个问题，我们开发了一种基于Transformer的模型，用于基于文本的维度情绪识别，并使用一个包含75503个样本的新颖、全面的维度情绪数据集进行微调。该模型在检测效价、唤醒和优势维度方面显著优于现有的最先进模型，皮尔逊相关系数分别达到r = 0.90、r = 0.77和r = 0.64。此外，一项涉及20名参与者的可行性研究证实了该模型在基于对话代理的iCBT环境中的技术有效性及其可用性、可接受性和同理心理解，标志着个性化和有效治疗体验有了实质性改善。
