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Unveiling topic dependencies through a multilevel topic model: a hierarchical approach to enhanced interpretability.

Youngsun Kim1, Hwan Chung2, Saebom Jeon3

  • 1School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Korea.

Journal of Applied Statistics
|April 6, 2026
PubMed
Summary

This study introduces a multilevel topic model (MTM) to uncover topic dependencies in text data. The MTM enhances topic interpretability by revealing relationships between themes, outperforming traditional methods like Latent Dirichlet Allocation.

Keywords:
62H1062H1262P25EM algorithmLatent Dirichlet allocationnonparametrictext miningtopic clustering

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Data Mining

Background:

  • Topic modeling identifies key themes in unstructured text data.
  • Latent Dirichlet Allocation (LDA) is a common topic model but fails to capture inter-topic dependencies.
  • Understanding topic relationships is crucial for deeper text analysis.

Purpose of the Study:

  • To propose a novel multilevel topic model (MTM) that captures hidden topic dependencies.
  • To allow word-based topic proportions to vary across a multilevel latent structure.
  • To improve the interpretability of topic modeling results.

Main Methods:

  • Developed a multilevel topic model (MTM) with a multilevel latent structure.
  • Employed a modified Expectation-Maximization (EM) algorithm with an upward-downward approach for parameter estimation.
  • Conducted empirical studies on corpora to validate the MTM.

Main Results:

  • The MTM successfully unearths hidden topic dependencies within a corpus.
  • Word-based topic proportions were shown to vary across the higher-level latent structure.
  • The multilevel hierarchy of the MTM was interpreted through empirical analyses.

Conclusions:

  • The proposed multilevel topic model (MTM) offers superior systematic interpretability compared to Latent Dirichlet Allocation.
  • MTM effectively reveals the inherent relationships and hierarchical structure between topics in text data.
  • This model provides a more nuanced understanding of thematic structures in unstructured text.