Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams

2026
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Authors: Federica Granese; Benjamin Navet; Serena Villata; Charles Bouveyron
Published in: Machine Learning and Knowledge Discovery in Databases. Research Track
Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the challenges and solutions for online topic modeling on data streams, focusing on the integration of optimal transport to enhance topic association and discovery. The authors introduce StreamETM, an online version of the Embedded Topic Model (ETM), which combines variational inference with a merging approach based on unbalanced optimal transport. The text highlights the importance of addressing the dynamic nature of topics, which evolve over time, and the need for automated change point detection to monitor significant shifts in data streams. Through extensive experiments, the authors demonstrate the superiority of their approach over traditional methods like Euclidean or Cosine similarities. The chapter also provides a comprehensive comparison with existing online topic modeling methods, showcasing the advantages of StreamETM in maintaining topic coherence and adapting to new data. The conclusion emphasizes the practical applications of StreamETM in providing synthetic summaries of document streams and issuing alerts for significant changes in topic dynamics.

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The online version contains supplementary material available at https://doi.org/10.1007/978-3-032-06109-6_17.

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Title: Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
Authors: Federica Granese
Benjamin Navet
Serena Villata
Charles Bouveyron
Publisher: Springer Nature Switzerland
Book: Machine Learning and Knowledge Discovery in Databases. Research Track
Print ISBN: 978-3-032-06108-9

Electronic ISBN: 978-3-032-06109-6

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-032-06109-6_17

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