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dc.contributor.authorKrause, Cedricen_US
dc.contributor.authorRieger, Jonasen_US
dc.contributor.authorFlossdorf, Jonathanen_US
dc.contributor.authorJentsch, Carstenen_US
dc.contributor.authorBeck, Fabianen_US
dc.contributor.editorGuthe, Michaelen_US
dc.contributor.editorGrosch, Thorstenen_US
dc.date.accessioned2023-09-25T11:38:04Z
dc.date.available2023-09-25T11:38:04Z
dc.date.issued2023
dc.identifier.isbn978-3-03868-232-5
dc.identifier.urihttps://doi.org/10.2312/vmv.20231231
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20231231
dc.description.abstractTexts 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visual analytics; Mathematics of computing → Time series analysis
dc.subjectHuman
dc.subjectcentered computing → Visual analytics
dc.subjectMathematics of computing → Time series analysis
dc.titleVisually Analyzing Topic Change Points in Temporal Text Collectionsen_US
dc.description.seriesinformationVision, Modeling, and Visualization
dc.description.sectionheadersImage Visualization and Analysis
dc.identifier.doi10.2312/vmv.20231231
dc.identifier.pages97-105
dc.identifier.pages9 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License