Visualizing Temporal-Thematic Patterns in Text Collections
Loading...
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Visualizing the temporal evolution of texts is relevant for many domains that seek to gain insight from text repositories. However, existing visualization methods for text collections do not show fine-grained temporal-thematic patterns. Therefore, we developed and analyzed a new visualization method that aims at uncovering such patterns. Specifically, we project texts to one dimension, which allows positioning texts in a 2D diagram of projection space and time. For projection, we employed two manifold learning algorithms: the self-organizing map (SOM) and UMAP. To assess the utility of our method, we experimented with real-world datasets and discuss the resulting visualizations. We find our method facilitates relating patterns and extracting associated texts beyond what is possible with previous techniques. We also conducted interviews with historians to show that our prototypical system supports domain experts in their analysis tasks.
Description
@inproceedings{10.2312:vmv.20211366,
booktitle = {Vision, Modeling, and Visualization},
editor = {Andres, Bjoern and Campen, Marcel and Sedlmair, Michael},
title = {{Visualizing Temporal-Thematic Patterns in Text Collections}},
author = {Knabben, Moritz and Baumann, Martin and Blascheck, Tanja and Ertl, Thomas and Koch, Steffen},
year = {2021},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-161-8},
DOI = {10.2312/vmv.20211366}
}