Bidirectional Topic Matching: Quantifying Thematic Overlap Between Corpora Through Topic Modelling

Raven Adam; Marie Lisa Kogler

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arXiv · 2412.18376

Bidirectional Topic Matching: Quantifying Thematic Overlap Between Corpora Through Topic Modelling

Raven Adam·Marie Lisa Kogler

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Abstract

This study introduces Bidirectional Topic Matching (BTM), a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate various topic modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet Allocation (LDA). BTM employs a dual-model approach, training separate topic models for each corpus and applying them reciprocally to enable comprehensive cross-corpus comparisons. This methodology facilitates the identification of shared themes and unique topics, providing nuanced insights into thematic relationships. Validation against cosine similarity-based methods demonstrates the robustness of BTM, with strong agreement metrics and distinct advantages in handling outlier topics. A case study on climate news articles showcases BTM's utility, revealing significant thematic overlaps and distinctions between corpora focused on climate change and climate action. BTM's flexibility and precision make it a valuable tool for diverse applications, from political discourse analysis to interdisciplinary studies. By integrating shared and unique topic analyses, BTM offers a comprehensive framework for exploring thematic relationships, with potential extensions to multilingual and dynamic datasets. This work highlights BTM's methodological contributions and its capacity to advance discourse analysis across various domains.

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Raven Adam, Marie Lisa Kogler. 2024-12-24. Bidirectional Topic Matching: Quantifying Thematic Overlap Between Corpora Through Topic Modelling. https://arxiv.org/abs/2412.18376

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