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arXiv Machine Learning By Brinda Murali Krishna, Oktay Karaku\c{s}, Can Eyupoglu

A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data

Read the original on arXiv Machine Learning →

The paper presents a new computational framework for modelling organisation-level semantic identity using longitudinal textual data. It combines semantic representation learning, graph-based modelling, and temporal analysis to create interpretable semantic fingerprints that capture diversity, concentration, connectivity, novelty, and community composition. The framework is validated on a corpus of K‑pop lyrics from major South Korean entertainment companies, revealing distinct, evolving semantic identities that are statistically robust and reproducible.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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