ComModeler: Topic Modeling Using Community Detection
| dc.contributor.author | Dang, Tommy | en_US |
| dc.contributor.author | Nguyen, Vinh The | en_US |
| dc.contributor.editor | Christian Tominski and Tatiana von Landesberger | en_US |
| dc.date.accessioned | 2018-06-02T17:56:56Z | |
| dc.date.available | 2018-06-02T17:56:56Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | This 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.sectionheaders | Analytics and Guidance | |
| dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | |
| dc.identifier.doi | 10.2312/eurova.20181104 | |
| dc.identifier.isbn | 978-3-03868-064-2 | |
| dc.identifier.pages | 1-5 | |
| dc.identifier.uri | https://doi.org/10.2312/eurova.20181104 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurova20181104 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | ComModeler: Topic Modeling Using Community Detection | en_US |