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

• URL: http://arxiv.org/abs/2412.18376v1
• Date: Tue, 24 Dec 2024 12:02:43 GMT
• Title: Bidirectional Topic Matching: Quantifying Thematic Overlap Between Corpora Through Topic Modelling
• Authors: Raven Adam, Marie Lisa Kogler,
• Abstract summary: Bidirectional Topic Matching (BTM) is a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM employs a dual-model approach, training separate topic models for each corpus and applying them reciprocally to enable comprehensive cross-corpus comparisons.
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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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