TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections

doi:10.1177/1473871618785390

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:

Paul, Celeste ^[1]; Chang, Jessica ^[1]; Endert, Alexander ^[2]; Cramer, Nicholas O. ^[3];

^[3]; Hampton, Shawn D. ^[3];

^[3]; Perko, Ralph J. ^[3]; Cook, Kris A. ^[4]

OTHER GOVERNMENT AGENCIES
Georgia Institute of Technology
BATTELLE (PACIFIC NW LAB)
Pacific Northwest National Laboratory

Publication Date:: 2019-07-01

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.

Copy to clipboard


                    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.

Copy to clipboard


                    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.

Copy to clipboard


                    
@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}

}

Copy to clipboard

Journal Article:

DOI: 10.1177/1473871618785390

Other availability

Find in Google Scholar

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

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

Paulovich, Fernando V.; Toledo, Franklina M. B.; Telles, Guilherme P.
Computer Graphics Forum, Vol. 31, Issue 3pt3
DOI: 10.1111/j.1467-8659.2012.03107.x

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

Similar records in OSTI.GOV collections:

Mixed Initiative Visual Analytics Using Task-Driven Recommendations

Conference Cook, Kristin A. ; Cramer, Nicholas O. ; Israel, David ; ...

Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support tasks involved in discovery and sensemaking, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems, at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with such analytic models, such as inferring data models from user interactions to steer the underlying modelsmore »« less
DOI: 10.1109/VAST.2015.7347625
Mining for Emerging Technologies within Text Streams and Documents

Conference Engel, David W. ; Whitney, Paul D. ; Calapristi, Augustin J. ; ...

Text streams, collections of documents or messages that are generated and observed over time, are ubiquitous. Our research and development is targeted at developing algorithms to find and characterize changes in topic within text streams. To date, this research has demonstrated the ability to detect and describe 1) short duration, atypical events and 2) the emergence of longer term shifts in topical content. This technology has been applied to pre-defined temporally ordered document collections but is also suitable for application to near real-time textual data streams. The underlying event and emergence detection algorithms have been interfaced to an event detectionmore »« less
Interaction Junk: User Interaction-Based Evaluation of Visual Analytic Systems

Conference Endert, Alexander ; North, Chris

With the growing need for visualization to aid users in understanding large, complex datasets, the ability for users to interact and explore these datasets is critical. As visual analytic systems have advanced to leverage powerful computational models and data analytics capabilities, the modes by which users engage and interact with the information are limited. Often, users are taxed with directly manipulating parameters of these models through traditional GUIs (e.g., using sliders to directly manipulate the value of a parameter). However, the purpose of user interaction in visual analytic systems is to enable visual data exploration – where users can focusmore »« less
DOI: 10.1145/2442576.2442583
Analytic Steering: Inserting Context into the Information Dialog

Conference Bohn, Shawn J. ; Calapristi, Augustin J. ; Brown, Shyretha D. ; ...

An analyst’s intrinsic domain knowledge is a primary asset in almost any analysis task. Unstructured text analysis systems that apply un-supervised content analysis approaches can be more effective if they can leverage this domain knowledge in a manner that augments the information discovery process without obfuscating new or unexpected content. Current unsupervised approaches rely upon the prowess of the analyst to submit the right queries or observe generalized document and term relationships from ranked or visual results. We propose a new approach which allows the user to control or steer the analytic view within the unsupervised space. This process ismore »« less
Linking Automated Data Analysis and Visualization with Applications in Developmental Biology and High-Energy Physics

Thesis/Dissertation Ruebel, Oliver

Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research coveredmore »« less
DOI: 10.2172/982922

Full Text Available

Similar Records

Title: TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections

Abstract

Citation Formats

A cartographic approach to visualizing conference abstracts journal, January 2002

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

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

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

Semantic Wordification of Document Collections journal, June 2012

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

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

Semantics of Directly Manipulating Spatializations journal, December 2013

Probabilistic Principal Component Analysis journal, August 1999

Dynamic Map Labeling journal, September 2006

A cartographic approach to visualizing conference abstracts
journal, January 2002

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

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

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

Semantic Wordification of Document Collections
journal, June 2012

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

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

Semantics of Directly Manipulating Spatializations
journal, December 2013

Probabilistic Principal Component Analysis
journal, August 1999

Dynamic Map Labeling
journal, September 2006