ComModeler: Topic Modeling Using Community Detection

dc.contributor.authorDang, Tommyen_US
dc.contributor.authorNguyen, Vinh Theen_US
dc.contributor.editorChristian Tominski and Tatiana von Landesbergeren_US
dc.date.accessioned2018-06-02T17:56:56Z
dc.date.available2018-06-02T17:56:56Z
dc.date.issued2018
dc.description.abstractThis paper introduces ComModeler, a novel approach for topic modeling using community finding in dynamic networks. Our algorithm first extracts the terms/keywords, formulates a network of collocated terms, then refines the network based on various features (such as term/relationship frequency, sudden changes in their frequency time series, or vertex betweenness centrality) to reveal the structures/communities in dynamic social networks. These communities correspond to different hidden topics in the input text documents. Although initially motivated to analyze text documents, we soon realized the ComModeler has more general implications for other application domains. We demonstrate the ComModeler on several real-world datasets, including the IEEE VIS publications from 1990 to 2016, together with collocated phrases obtained from various political blogs.en_US
dc.description.sectionheadersAnalytics and Guidance
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20181104
dc.identifier.isbn978-3-03868-064-2
dc.identifier.pages1-5
dc.identifier.urihttps://doi.org/10.2312/eurova.20181104
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20181104
dc.publisherThe Eurographics Associationen_US
dc.titleComModeler: Topic Modeling Using Community Detectionen_US
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