Interplay of Visual Analytics and Topic Modeling in Gameplay Analysis

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dc.contributor.authorMoussavi, Lalehen_US
dc.contributor.authorAndrienko, Gennadyen_US
dc.contributor.authorAndrienko, Nataliaen_US
dc.contributor.authorSlingsby, Aidanen_US
dc.contributor.editorHunter, Daviden_US
dc.contributor.editorSlingsby, Aidanen_US
dc.date.accessioned2024-09-09T05:44:42Z
dc.date.available2024-09-09T05:44:42Z
dc.date.issued2024
dc.description.abstractSpatio-temporal event sequences consist of activities or occurrences involving various interconnected elements in space and time. Exploring these sequences with topic modeling is a relatively new and evolving research area. We use topic modeling to analyze football games, as an example of complex and under-explored spatio-temporal event data. A key challenge in topic modeling is selecting the most suitable number of topics for the downstream application. Selecting too few topics oversimplifies the data, merging distinct patterns, whereas selecting too many can fragment coherent themes into overlapping categories. We propose a visual analytics technique that uses dimensionality reduction on topics derived from multiple topic modeling runs, each with a different number of topics. Our technique organizes all the topics in a hierarchical layout based on their spatial similarity, making it easier to make an informed decision about selecting the most expressive set of topics that represent distinctive spatial patterns. We apply our visual analytics technique to a football dataset, illustrating how it can be used to select an appropriate set of topics for this data. We then use these topics to represent game episodes, which help us summarize game dynamics and uncover insights into the games.en_US
dc.description.sectionheadersVisualisation Design and Evaluation Methods
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20241213
dc.identifier.isbn978-3-03868-249-3
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.2312/cgvc.20241213
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/cgvc20241213
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
dc.subjectHuman centered computing → Visual Analytics
dc.titleInterplay of Visual Analytics and Topic Modeling in Gameplay Analysisen_US
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