# Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces

arXiv: https://arxiv.org/abs/2509.01051 · HTML: https://mlsanity.com/paper/2509.01051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Parametric UMAP embeddings for representation and semi-supervised learning](https://mlsanity.com/paper/2009.12981/) (2020)
- [Dynamic Word Embeddings](https://mlsanity.com/paper/1702.08359/) (2017)
- [BERTrend: Neural Topic Modeling for Emerging Trends Detection](https://mlsanity.com/paper/2411.05930/) (2024)
- [Visualizing Temporal Topic Embeddings with a Compass](https://mlsanity.com/paper/2409.10649/) (2024)

## Abstract (arXiv)

Many real-world datasets -- from an artist's body of work to a person's social media history -- exhibit meaningful semantic changes over time that are difficult to capture with existing dimensionality reduction methods. To address this gap, we introduce a visualization technique that combines force-based projection and streaming clustering methods to build a spatial-temporal map of embeddings. Applying this technique, we create Chronotome, a tool for interactively exploring evolving themes in time-based data -- in real time. We demonstrate the utility of our approach through use cases on text and image data, showing how it offers a new lens for understanding the aesthetics and semantics of temporal datasets.