Written texts reflect an author's perspective, making the thorough analysis of literature a key research method in fields such as the humanities and social sciences. However, conventional text mining techniques like sentiment analysis and topic modeling are limited in their ability to capture the hierarchical narrative structures that reveal deeper argumentative patterns. To address this gap, we propose a method that leverages large language models (LLMs) to extract and organize these structures into a hierarchical framework. We validate this approach by analyzing public opinions on generative AI collected by Japan's Agency for Cultural Affairs, comparing the narratives of supporters and critics. Our analysis provides clearer visualization of the factors influencing divergent opinions on generative AI, offering deeper insights into the structures of agreement and disagreement.

翻译：书面文本反映了作者的视角，这使得对文献的深入分析成为人文与社会科学等领域的关键研究方法。然而，传统的文本挖掘技术（如情感分析和主题建模）在捕捉揭示更深层论证模式的层次化叙事结构方面存在局限。为弥补这一不足，我们提出一种利用大语言模型（LLMs）提取这些结构并将其组织成层次化框架的方法。我们通过分析日本文化厅收集的关于生成式人工智能的公众意见，比较支持者与批评者的叙事，验证了该方法的有效性。我们的分析更清晰地可视化了影响生成式人工智能对立意见的因素，从而为理解共识与分歧的结构提供了更深入的见解。