Emotional coordination is a core property of human interaction that shapes how relational meaning is constructed in real time. While text-based affect inference has become increasingly feasible, prior approaches often treat sentiment as a deterministic point estimate for individual speakers, failing to capture the inherent subjectivity, latent ambiguity, and sequential coupling found in mutual exchanges. We introduce LLM-MC-Affect, a probabilistic framework that characterizes emotion not as a static label, but as a continuous latent probability distribution defined over an affective space. By leveraging stochastic LLM decoding and Monte Carlo estimation, the methodology approximates these distributions to derive high-fidelity sentiment trajectories that explicitly quantify both central affective tendencies and perceptual ambiguity. These trajectories enable a structured analysis of interpersonal coupling through sequential cross-correlation and slope-based indicators, identifying leading or lagging influences between interlocutors. To validate the interpretive capacity of this approach, we utilize teacher-student instructional dialogues as a representative case study, where our quantitative indicators successfully distill high-level interaction insights such as effective scaffolding. This work establishes a scalable and deployable pathway for understanding interpersonal dynamics, offering a generalizable solution that extends beyond education to broader social and behavioral research.

翻译：情感协调是人类互动的核心属性，它塑造了关系意义在实时交流中的构建方式。尽管基于文本的情感推断已日益可行，但现有方法通常将情感视为对话者个体的确定性点估计，未能捕捉相互交流中存在的主观性、潜在歧义及序列耦合特性。本文提出LLM-MC-Affect概率框架，该框架将情感表征为情感空间上的连续潜在概率分布，而非静态标签。通过利用随机化LLM解码与蒙特卡洛估计方法，该方法近似计算这些分布，从而推导出既能量化核心情感倾向，又能体现感知歧义的高保真情感轨迹。基于这些轨迹，我们通过序列互相关和斜率指标对人际耦合进行结构化分析，识别对话者之间的引领或滞后影响。为验证该方法的解释能力，本研究以师生教学对话为典型案例，通过量化指标成功提取出有效支架式教学等高层次互动洞见。该工作为理解人际动态建立了可扩展且可部署的实施路径，提供了可泛化至教育领域之外、适用于更广泛社会与行为研究的通用解决方案。