Visualizing Temporal Topic Embeddings with a Compass

TL;DR

This paper introduces a novel method for visualizing and analyzing the evolution of topics over time by directly embedding words and documents in a shared temporal space, enhancing interpretability.

Contribution

It extends the compass-aligned temporal Word2Vec approach to dynamic topic modeling, enabling direct comparison of embeddings across time within a unified framework.

Findings

Competitive performance in topic relevancy and diversity

Provides insightful visualizations of temporal word embeddings

Maintains global topic evolution insights

Abstract

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.