skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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 paper we describe TexTonic’s data processing and analytic pipeline, user interface and interaction design principles, and the 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

Authors:
 [1];  [1];  [2];  [3]; ORCiD logo [3];  [3]; ORCiD logo [3];  [3];  [4]
  1. OTHER GOVERNMENT AGENCIES
  2. Georgia Institute of Technology
  3. BATTELLE (PACIFIC NW LAB)
  4. Pacific Northwest National Laboratory
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1576230
Report Number(s):
PNNL-SA-124019
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Information Visualization
Additional Journal Information:
Journal Volume: 18; Journal Issue: 3
Country of Publication:
United States
Language:
English
Subject:
human centered computing, Visual analytics, Information systems, document collection models

Citation Formats

Paul, Celeste, Chang, Jessica, Endert, Alexander, Cramer, Nicholas O., Gillen, David S., Hampton, Shawn D., Burtner, Edwin R., Perko, Ralph J., and Cook, Kris A. TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections. United States: N. p., 2019. Web. doi:10.1177/1473871618785390.
Paul, Celeste, Chang, Jessica, Endert, Alexander, Cramer, Nicholas O., Gillen, David S., Hampton, Shawn D., Burtner, Edwin R., Perko, Ralph J., & Cook, Kris A. TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections. United States. doi:10.1177/1473871618785390.
Paul, Celeste, Chang, Jessica, Endert, Alexander, Cramer, Nicholas O., Gillen, David S., Hampton, Shawn D., Burtner, Edwin R., Perko, Ralph J., and Cook, Kris A. Mon . "TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections". United States. doi:10.1177/1473871618785390.
@article{osti_1576230,
title = {TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections},
author = {Paul, Celeste and Chang, Jessica and Endert, Alexander and Cramer, Nicholas O. and Gillen, David S. and Hampton, Shawn D. and Burtner, Edwin R. and Perko, Ralph J. and Cook, Kris A.},
abstractNote = {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 paper we describe TexTonic’s data processing and analytic pipeline, user interface and interaction design principles, and the 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},
doi = {10.1177/1473871618785390},
journal = {Information Visualization},
number = 3,
volume = 18,
place = {United States},
year = {2019},
month = {7}
}

Works referenced in this record:

A cartographic approach to visualizing conference abstracts
journal, January 2002

  • Skupin, A.
  • IEEE Computer Graphics and Applications, Vol. 22, Issue 1
  • DOI: 10.1109/38.974518

TextTile]]>: An Interactive Visualization Tool for Seamless Exploratory Analysis of Structured Data and Unstructured Text
journal, January 2017

  • Felix, Cristian; Pandey, Anshul Vikram; Bertini, Enrico
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 23, Issue 1
  • DOI: 10.1109/TVCG.2016.2598447

TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections
journal, January 2017

  • Kim, Minjeong; Kang, Kyeongpil; Park, Deokgun
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 23, Issue 1
  • DOI: 10.1109/TVCG.2016.2598445

Visualization of a document collection: The vibe system
journal, January 1993

  • Olsen, Kai A.; Korfhage, Robert R.; Sochats, Kenneth M.
  • Information Processing & Management, Vol. 29, Issue 1
  • DOI: 10.1016/0306-4573(93)90024-8

Semantic Wordification of Document Collections
journal, June 2012


Analyzing and visualizing the semantic coverage of Wikipedia and its authors
journal, January 2007

  • Holloway, Todd; Bozicevic, Miran; Börner, Katy
  • Complexity, Vol. 12, Issue 3
  • DOI: 10.1002/cplx.20164

Beyond Control Panels: Direct Manipulation for Visual Analytics
journal, July 2013

  • Endert, A.; Bradel, L.; North, C.
  • IEEE Computer Graphics and Applications, Vol. 33, Issue 4
  • DOI: 10.1109/MCG.2013.53

Semantics of Directly Manipulating Spatializations
journal, December 2013

  • Xinran Hu, ; Bradel, Lauren; Maiti, Dipayan
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 19, Issue 12
  • DOI: 10.1109/TVCG.2013.188

Probabilistic Principal Component Analysis
journal, August 1999

  • Tipping, Michael E.; Bishop, Christopher M.
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 61, Issue 3, p. 611-622
  • DOI: 10.1111/1467-9868.00196

Dynamic Map Labeling
journal, September 2006

  • Been, Ken; Daiches, Eli; Yap, Chee
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 12, Issue 5
  • DOI: 10.1109/TVCG.2006.136