TopicNets
Abstract
We present TopicNets , a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. A range of visualization types and control mechanisms to support knowledge discovery are presented. These include corpus- and document-specific views, iterative topic modeling, search, and visual filtering. Drill-down functionality is provided to allow analysts to visualize individual document sections and their relations within the global topic space. Analysts can search across a dataset through a set of expansion techniques on selected document and topic nodes. Furthermore, analysts can select relevant subsets of documents and perform real-time topic modeling on these subsets to interactively visualize topics at various levels of granularity, allowing for a better understanding of the documents. A discussion of the design and implementation choices for each visual analysis technique is presented. This is followed by a discussion of three diverse use cases in which TopicNets enables fast discovery of information that is otherwise hard to find. These include a corpus of 50,000 successful NSF grant proposals, 10,000 publications from a large research center, and single documents including a grant proposal and a PhD thesis.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 01, 2012
- Source ID
- 10.1145/2089094.2089099
Entities
People
- Arthur Asuncion
- Brynjar Gretarsson
- David Newman
- John O’donovan
- Padhraic Smyth
- Svetlin Bostandjiev
- Tobias Hollerer
Organizations
- Army Research Office
- Division of Computer and Network Systems
- Division of Information and Intelligent Systems
- United States Army Research Laboratory
- University of California, Irvine
- University of California, Santa Barbara