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

• URL: http://arxiv.org/abs/2504.07711v2
• Date: Wed, 22 Oct 2025 10:21:29 GMT
• Title: Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
• Authors: Federica Granese, Benjamin Navet, Serena Villata, Charles Bouveyron,
• Abstract summary: StreamETM builds on the Embedded Topic Model (ETM) to handle data streams.<n>An online change point detection algorithm is employed to identify shifts in topics over time.<n> Numerical experiments on simulated and real-world data show StreamETM outperforming competitors.
• Score: 8.618304780146348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making online topic modeling methods essential for managing these data streams that continuously arrive over time. This paper introduces a novel approach to online topic modeling named StreamETM. This approach builds on the Embedded Topic Model (ETM) to handle data streams by merging models learned on consecutive partial document batches using unbalanced optimal transport. Additionally, an online change point detection algorithm is employed to identify shifts in topics over time, enabling the identification of significant changes in the dynamics of text streams. Numerical experiments on simulated and real-world data show StreamETM outperforming competitors. We provide the code publicly available at https://github.com/fgranese/StreamETM.

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