Sentiment Simulation using Generative AI Agents

• URL: http://arxiv.org/abs/2505.22125v1
• Date: Wed, 28 May 2025 08:50:56 GMT
• Title: Sentiment Simulation using Generative AI Agents
• Authors: Melrose Tia, Jezreel Sophia Lanuzo, Lei Rigi Baltazar, Marie Joy Lopez-Relente, Diwa Malaya Quiñones, Jason Albia,
• Abstract summary: We present a framework for sentiment simulation using generative AI agents embedded with psychologically rich profiles.<n>Agents are instantiated from a nationally representative survey of 2,485 Filipino respondents.<n>Our findings establish a scalable framework for sentiment modeling through psychographically grounded AI agents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traditional sentiment analysis relies on surface-level linguistic patterns and retrospective data, limiting its ability to capture the psychological and contextual drivers of human sentiment. These limitations constrain its effectiveness in applications that require predictive insight, such as policy testing, narrative framing, and behavioral forecasting. We present a robust framework for sentiment simulation using generative AI agents embedded with psychologically rich profiles. Agents are instantiated from a nationally representative survey of 2,485 Filipino respondents, combining sociodemographic information with validated constructs of personality traits, values, beliefs, and socio-political attitudes. The framework includes three stages: (1) agent embodiment via categorical or contextualized encodings, (2) exposure to real-world political and economic scenarios, and (3) generation of sentiment ratings accompanied by explanatory rationales. Using Quadratic Weighted Accuracy (QWA), we evaluated alignment between agent-generated and human responses. Contextualized encoding achieved 92% alignment in replicating original survey responses. In sentiment simulation tasks, agents reached 81%--86% accuracy against ground truth sentiment, with contextualized profile encodings significantly outperforming categorical (p < 0.0001, Cohen's d = 0.70). Simulation results remained consistent across repeated trials (+/-0.2--0.5% SD) and resilient to variation in scenario framing (p = 0.9676, Cohen's d = 0.02). Our findings establish a scalable framework for sentiment modeling through psychographically grounded AI agents. This work signals a paradigm shift in sentiment analysis from retrospective classification to prospective and dynamic simulation grounded in psychology of sentiment formation.

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