Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces

Keywords

Dynamic topic modeling, embedding visualization, clustering methods, temporal data, spring force models

Abstract

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.