用频域与量子方法建模辩论中观点变化，提升决策分析能力。
Capturing Opinion Shifts in Deliberative Discourse through Frequency-based Quantum deep learning methods
- 基于频率与量子机制建模话语演化过程
- 新框架在观点转变预测上优于现有模型
- 适合政策制定与社交媒体舆论分析场景
辩论通过权衡多元观点来影响最终决策。随着自然语言处理技术进步，可计算地建模辩论过程，分析观点演变并预测不同情境下的结果。本研究对比多种NLP方法，评估其对辩论话语的理解与洞察力。收集了来自不同背景个体的观点，构建自源数据集以反映多样视角。通过富含关键事实的产品演示模拟辩论，常引发可测量的观众观点转变。比较了两种模型：频域话语调制（Frequency-Based Discourse Modulation）与量子辩论框架（Quantum-Deliberation Framework），后者表现优于现有先进模型。研究结果表明该方法在公共政策制定、辩论评估、决策支持系统及大规模社交媒体舆论挖掘中有实际应用价值。
原文摘要 · Abstract (English)
Deliberation plays a crucial role in shaping outcomes by weighing diverse perspectives before reaching decisions. With recent advancements in Natural Language Processing, it has become possible to computationally model deliberation by analyzing opinion shifts and predicting potential outcomes under varying scenarios. In this study, we present a comparative analysis of multiple NLP techniques to evaluate how effectively models interpret deliberative discourse and produce meaningful insights. Opinions from individuals of varied backgrounds were collected to construct a self-sourced dataset that reflects diverse viewpoints. Deliberation was simulated using product presentations enriched with striking facts, which often prompted measurable shifts in audience opinions. We have given comparative analysis between two models namely Frequency-Based Discourse Modulation and Quantum-Deliberation Framework which outperform the existing state of art models. The findings highlight practical applications in public policy-making, debate evaluation, decision-support frameworks, and large-scale social media opinion mining.
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