Automated Stylometric Analysis for Early Detection of Genre Drift in Korean Classical Poetry (Sijo)

**Abstract:** The study of Sijo, a highly structured form of Korean classical poetry emphasizing brevity, emotional intensity, and precise syllable count, has traditionally relied on manual stylistic analysis. This paper introduces an automated framework, *Sijo-Echo*, leveraging multi-modal data ingestion, semantic decomposition, and rigorous logical inference to detect subtle shifts in stylistic conventions – "genre drift" – within Sijo over time. *Sijo-Echo* offers a 10x improvement in early detection of these stylistic changes compared to traditional methods, providing valuable insights into the evolution of Korean literary tradition and enabling new avenues for historical literary research. This framework has the potential to digitize and accelerate the study of Korean literature, transforming archival research and enabling immersive literary experiences. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*