Abstract:
Topic model has been an active research area for many years, it can be used for discovering latent semantics and finding hidden knowledge in unstructured data corpus. In ...Show MoreMetadata
Abstract:
Topic model has been an active research area for many years, it can be used for discovering latent semantics and finding hidden knowledge in unstructured data corpus. In this paper, we investigated the problems in visualizing hierarchical topic and their evolution. The contribution of this paper is threefold, first we explore the static visualization of hierarchical topics using the `nested circle' layout, and then in order to present the topic evolution over time, we extended a hierarchical topic model and employ topic transformation visualizations to track the arising, splitting and disappearing of certain topics under the dynamic topical hierarchy. Finally, a Hierarchical Topic Model Visualization System (HTMVS) is designed to take advantage of both static and dynamic hierarchical topic visualization.
Date of Conference: 25-30 October 2015
Date Added to IEEE Xplore: 07 December 2015
Electronic ISBN:978-1-4673-9783-4