Capturing Opinion Shifts in Deliberative Discourse through Frequency-based Quantum deep learning methods
This work addresses the problem of analyzing deliberation for applications like public policy-making and social media opinion mining, but it appears incremental as it compares methods without introducing a fundamentally new approach.
The study tackled the problem of modeling opinion shifts in deliberative discourse by comparing NLP techniques, finding that Frequency-Based Discourse Modulation and Quantum-Deliberation Framework outperform existing state-of-the-art models.
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.