TexTonic: Interactive visualization for exploration and discovery of very large text collections
Abstract
TexTonic is a visual analytic system for interactive exploration of very large unstructured text collections. TexTonic visualizes hierarchical clusters of representative terms, snippets, and documents in a single, multi-scale spatial layout. Exploration is supported by interacting with the visualization and directly manipulating the terms in the visualization using semantic interactions. These semantic interactions steer the underlying analytic model by translating user interactions within the visualization to contextual updates to the supporting data model. The combination of semantic interactions and information visualization at multiple levels of the data hierarchy helps users manage information overload so that they can more effectively explore very large text collections. In this article, we describe TexTonic’s data processing and analytic pipeline, user interface and interaction design principles, and results of a user study conducted mid-development with experienced data analysts. We also discuss the implications TexTonic could have on visual exploration and discovery tasks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 12, 2018
- Source ID
- 10.1177/1473871618785390
Entities
People
- Alex Endert
- Celeste Lyn Paul
- David Gillen
- Jessica Chang
- Kristin A Cook
- Nick Cramer
- Ralph Perko
- Russ Burtner
- Shawn Hampton
Organizations
- Georgia Tech
- Pacific Northwest National Laboratory
- United States Department of Defense