情绪调节引导叙事中基于心理成分的情绪识别
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation
AI总结：
本文引入法语情绪叙事语料库，基于 Component Process Model 理论框架，利用机器学习与预训练语言模型研究心理成分对情绪分类的影响，发现联合所有成分可取得最佳预测效果。
摘要：
情绪调节是应对情绪事件的关键要素，对心理健康有积极影响。本文旨在通过引入一个使用情绪调节问卷收集的法语情绪叙事新语料库，为情绪事件提供更全面的理解。我们遵循 Component Process Model 的理论框架，该框架将情绪视为由四个相互关联的成分（行为、感受、思考和领地）组成的动态过程。每个叙事都与一种离散情绪相关，并由作者基于所有情绪成分进行结构化。我们利用机器学习方法和预训练语言模型研究了各成分的相互作用及其对情绪分类的影响。结果表明，每个成分都能提升预测性能，而联合考虑所有成分可取得最佳结果。结果还表明，预训练语言模型在从特定成分预测离散情绪方面非常有效，揭示了情绪成分表达方式的差异。
英文摘要：
Emotion regulation is a crucial element in dealing with emotional events and has positive effects on mental health. This paper aims to provide a more comprehensive understanding of emotional events by introducing a new French corpus of emotional narratives collected using a questionnaire for emotion regulation. We follow the theoretical framework of the Component Process Model which considers emotions as dynamic processes composed of four interrelated components (behavior, feeling, thinking and territory). Each narrative is related to a discrete emotion and is structured based on all emotion components by the writers. We study the interaction of components and their impact on emotion classification with machine learning methods and pre-trained language models. Our results show that each component improves prediction performance, and that the best results are achieved by jointly considering all components. Our results also show the effectiveness of pre-trained language models in predicting discrete emotion from certain components, which reveal differences in how emotion components are expressed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
