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互动中的构建方式。尽管基于文本的情感推断已日益可行，但先前的方法通常将情感视为个体说话者的确定性点估计，未能捕捉相互交流中固有的主观性、潜在模糊性和序列耦合。我们提出了LLM-MC-Affect，一个概率框架，该框架将情感不是表征为静态标签，而是定义为在情感空间上的连续潜在概率分布。通过利用随机LLM解码和蒙特卡洛估计，该方法近似这些分布以推导出高保真的情感轨迹，这些轨迹明确量化了中心情感趋势和感知模糊性。这些轨迹通过序列互相关和基于斜率的指标，实现了对人际耦合的结构化分析，识别出对话者之间的主导或滞后影响。为验证该方法的解释能力，我们以师生教学对话作为代表性案例研究，其中我们的量化指标成功提炼出如有效支架等高层次的互动洞察。这项工作为理解人际动态建立了一条可扩展且可部署的路径，提供了一个可推广的解决方案，其应用范围从教育领域延伸至更广泛的社会与行为研究。