LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight

2026-01-07

arXiv.org

AI Summary

This work proposes a Monte Carlo modeling framework grounded in large language models to address the limitations of traditional sentiment analysis, which oversimplifies emotions as deterministic labels and fails to capture the subjectivity, ambiguity, and sequential coupling inherent in interpersonal interactions. By modeling emotions as continuous latent probability distributions, the approach leverages stochastic decoding and Monte Carlo estimation to generate high-fidelity emotional dynamics trajectories. The framework further introduces interpretable cross-correlation and slope metrics to quantify emotional lead-lag relationships between interlocutors. Applied to teacher-student dialogues, the method successfully identifies high-level interaction patterns such as scaffolding instruction, demonstrating strong interpretability and generalization in uncovering the nuanced dynamics of interpersonal affect.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

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.

Problem

• emotional coordination
• latent ambiguity
• interpersonal dynamics
• affective trajectories
• sentiment inference

Innovation

• LLM-MC-Affect
• Monte Carlo estimation
• affective trajectories
• latent ambiguity
• interpersonal dynamics