Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams
This work addresses the challenge of analyzing continuously evolving document streams, such as news articles, which is an incremental improvement over existing online topic models.
The paper tackles the problem of online topic modeling for evolving document streams by proposing SB-SETM, which extends the Embedded Topic Model to automatically infer active topics and merge topic embeddings using continuous optimal transport, showing improved performance over baselines in simulated scenarios and on a real-world news corpus about the Russian-Ukrainian war.
Online topic models are unsupervised algorithms to identify latent topics in data streams that continuously evolve over time. Although these methods naturally align with real-world scenarios, they have received considerably less attention from the community compared to their offline counterparts, due to specific additional challenges. To tackle these issues, we present SB-SETM, an innovative model extending the Embedded Topic Model (ETM) to process data streams by merging models formed on successive partial document batches. To this end, SB-SETM (i) leverages a truncated stick-breaking construction for the topic-per-document distribution, enabling the model to automatically infer from the data the appropriate number of active topics at each timestep; and (ii) introduces a merging strategy for topic embeddings based on a continuous formulation of optimal transport adapted to the high dimensionality of the latent topic space. Numerical experiments show SB-SETM outperforming baselines on simulated scenarios. We extensively test it on a real-world corpus of news articles covering the Russian-Ukrainian war throughout 2022-2023.