ABSTRACT In this paper, an algorithm model is proposed to describe the emotion regulation process of human beings based on Hidden Markov Model (HMM). The process of reappraisal strategy in emotion regulation is optimized by an individual’s personality and the effect of interaction between emotions. Firstly, the theoretical bases in this paper, the theory of Gross emotion regulation process and the Five-Factor Model are described and the relationship between psychology and mathematics models is built by analysing Gross emotion regulation process and HMM. The interaction between emotions affects the initial matrix values by emotional state transition probability. Afterwards, how significantly the personality effects work on reappraisal strategies is elaborated and how to quantify them is discussed. The transition probability matrix based on emotional states is changed with the positions of external emotional stimuli in emotional space and the personality factors of individuals. The simulation results show that the model is effective. The results show that the algorithm can improve the emotion generation and expression in the field of emotion computing in human–computer interaction process and realize the real machine autonomous emotion calculation.

中文翻译：

---

情绪调节中的人格再评价模型

摘要 本文提出了一种基于隐马尔可夫模型（HMM）来描述人类情绪调节过程的算法模型。情绪调节中的重估策略过程是通过个体的个性和情绪相互作用的效果来优化的。首先,阐述了本文的理论基础、大情绪调节过程理论和五因素模型,通过对大情绪调节过程和HMM的分析,建立了心理学与数学模型之间的关系。情绪之间的相互作用通过情绪状态转移概率影响初始矩阵值。之后，详细阐述了人格影响对重估策略的影响程度以及如何量化它们。基于情绪状态的转移概率矩阵随着外部情绪刺激在情绪空间中的位置和个体的人格因素而变化。仿真结果表明该模型是有效的。结果表明，该算法能够改善人机交互过程中情感计算领域的情感生成和表达，实现真机自主情感计算。