Visually Analyzing Topic Change Points in Temporal Text Collections
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Texts are collected over time and reflect temporal changes in the themes that they cover. While some changes might slowly evolve, other changes abruptly surface as explicit change points. In an application study for a change point extraction method based on a rolling Latent Dirichlet Allocation (LDA), we have developed a visualization approach that allows exploring such change points and related change patterns. Our visualization not only provides an overview of topics, but supports the detailed exploration of temporal developments. The interplay of general topic contents, development, and similarities with detected change points reveals rich insights into different kinds of change patterns. The approach comprises a combination of views including topic timeline representations with detected change points, comparative word clouds, and temporal similarity matrices. In an interactive exploration, these views adapt to selected topics, words, or points in time. We demonstrate the use cases of our approach in an in-depth application example involving statisticians.
Description
CCS Concepts: Human-centered computing → Visual analytics; Mathematics of computing → Time series analysis
@inproceedings{10.2312:vmv.20231231,
booktitle = {Vision, Modeling, and Visualization},
editor = {Guthe, Michael and Grosch, Thorsten},
title = {{Visually Analyzing Topic Change Points in Temporal Text Collections}},
author = {Krause, Cedric and Rieger, Jonas and Flossdorf, Jonathan and Jentsch, Carsten and Beck, Fabian},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-232-5},
DOI = {10.2312/vmv.20231231}
}