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Visual Analytics for the Digital Humanities:
Combining Analytics and Visualization for Gaining
Insights into Linguistic Data

Daniel A. Keim
Data Analysis and Information
Visualization Group
University of Konstanz, Germany

Herrenhausen Conference, Hannover, Germany
December 5, 2013

1
Visual Analytics

"Computers are incredibly fast,
accurate, and stupid; humans are
incredibly slow, inaccurate, and
brilliant; together they are powerful
beyond imagination."
attributed to Albert Einstein

Visual Analytics
Tight Integration of Visual and Automatic Data Analysis Methods
for Information Exploration and Scalable Decision Support

Visual Data Exploration
Visualization
Data

Knowledge
Models
Automated Data Analysis
Feedback loop

2
Visual Analytics

Roadmap from the
VisMaster EU Project

www.visual-analytics.eu

Video

3
Why Visualization for the Digital Humanities?
•! Automated techniques not sufficient
–! Data ambiguous and incomplete
–! Complex relationship
–! Semantic gap
–! Limited Accuracy

•! Human Interaction is central for
–! Exploration of Data
–! Generation of Hypotheses
–! Interpretation of Results
–! Steering of the Analysis

Outline
•! Visual Analytics
–! Motivation and Definition
–! Visualization for the e-Humanities

•! Visual Analytics Examples
–! Literature Analysis
–! Language Analysis
–! Political Analysis

•! Perspectives

4
Autorship Attribution

Books of Mark Twain

Books of Jack London

Autorship Attribution

Average
or
Development
over the text?

5
Literature Fingerprinting

Book of
Jack
London
Book of
Mark
Twain

One Book

One block of 10000 words

6
7
Age Suitability Analysis

Features

Characters (Part of Harry Potter)

–! Character Detection
–! Topic Detection
–! Emotion Detection
–! Story Complexity
–! Book Features
–! Readability

Characters (Part of Stephen King’s “It”)

Character are, for example,
(1) Named Entities (2) often agents of verbs
(3) usually not after prepositions indicating a location

Age Suitability Analysis

8
Outline
•! Visual Analytics
–! Motivation and Definition
–! Visualization for the e-Humanities

•! Visual Analytics Examples
–! Literature Analysis
–! Language Analysis
–! Political Analysis

•! Perspectives

Cross-Language Analysis

9
Cross-Language Analysis

Languages from Papua New Guinea with leaves showing features
ordered to maximize (left) and minimize (right) the pairwise leaf similarity

Cross-Language Analysis

10
Vowel Harmony: Cross-linguistic Comparison
of Complex Language Features

“two-level” Vowel Harmony

i and u avoid each other

“one-level” Vowel Harmony

syllable reduplication

Vowel succession patterns in 42 languages (automatically
sorted by significance) [2]

Vowel Harmony: Cross-linguistic Comparison
of Complex Language Features

Comparing Swedish and Norwegian: Vowel
transitions according to their position within words
based on at least 50 Bible types.

Vowel transitions according to their position within
words. Only those transitions plotted based on at least
200 Bible types (interactive filter).

11
Tracking Semantic Change
Frequency development of different word senses automatically induced from word contexts with topic modeling.
Data: NYT Annotated Corpus, 1.8 million articles from daily newspaper editions 1987-2007

Reprinted from [3], © 2011 Association for Computational Linguistics

Analyzing Prosodic Features: Intonation

12
Analyzing Prosodic Features: Intonation

Outline
•! Visual Analytics
–! Motivation and Definition
–! Visualization for the e-Humanities

•! Visual Analytics Examples
–! Literature Analysis
–! Language Analysis
–! Political Analysis

•! Perspectives

13
One day of the
Stuttgart 21
mediations

BMBF Project
VisArgue

Presidential
Debate
Analysis

BMBF Project
VisArgue

14
Presidential
Debate
Analysis

Topic Shifts
BMBF Project
VisArgue

Presidential
Debate
Analysis

Crosstalk
BMBF Project
VisArgue

15
Comparison of
US-Presidential
Debates

Obama vs. McCain
2008

Obama vs. Romney
2012

BMBF Project
VisArgue

Stuttgart 21
Discourse
Analysis

BMBF Project
VisArgue

16
Analysis of Policy Networks

Parallel Tag Clouds to Show Differences
across US Court Circuits

Reprinted from Collins et al. [9], © 2009 IEEE

17
Voronoi Treemaps [10] in NYT

http://www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html?_r=0

Outline
•! Visual Analytics
–! Motivation and Definition
–! Visualization for the e-Humanities

•! Visual Analytics Examples
–! Literature Analysis
–! Language Analysis
–! Political Analysis

•! Perspectives

18
Visualization in the Digital Humanities
•! Visualization is central to allow humans and computers
to cooperate effectively
–! allow the computer to process large data
–! allow the human to understand and interact with large data

•! Interactive Visualization is central for
–! Exploration of Data
–! Interpretation of Results
–! Generation of Hypotheses
–! Steering of the Analysis

19
Thank you for your attention.
Questions?
“Anyone who claims to know all the answers
doesn't really know very much.”
Apostle Paul in 1. Cor. 8,2

infovis.uni-konstanz.de

20

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Digital humanitiesherrenhaeusserforum2013keim

  • 1. Visual Analytics for the Digital Humanities: Combining Analytics and Visualization for Gaining Insights into Linguistic Data Daniel A. Keim Data Analysis and Information Visualization Group University of Konstanz, Germany Herrenhausen Conference, Hannover, Germany December 5, 2013 1
  • 2. Visual Analytics "Computers are incredibly fast, accurate, and stupid; humans are incredibly slow, inaccurate, and brilliant; together they are powerful beyond imagination." attributed to Albert Einstein Visual Analytics Tight Integration of Visual and Automatic Data Analysis Methods for Information Exploration and Scalable Decision Support Visual Data Exploration Visualization Data Knowledge Models Automated Data Analysis Feedback loop 2
  • 3. Visual Analytics Roadmap from the VisMaster EU Project www.visual-analytics.eu Video 3
  • 4. Why Visualization for the Digital Humanities? •! Automated techniques not sufficient –! Data ambiguous and incomplete –! Complex relationship –! Semantic gap –! Limited Accuracy •! Human Interaction is central for –! Exploration of Data –! Generation of Hypotheses –! Interpretation of Results –! Steering of the Analysis Outline •! Visual Analytics –! Motivation and Definition –! Visualization for the e-Humanities •! Visual Analytics Examples –! Literature Analysis –! Language Analysis –! Political Analysis •! Perspectives 4
  • 5. Autorship Attribution Books of Mark Twain Books of Jack London Autorship Attribution Average or Development over the text? 5
  • 6. Literature Fingerprinting Book of Jack London Book of Mark Twain One Book One block of 10000 words 6
  • 7. 7
  • 8. Age Suitability Analysis Features Characters (Part of Harry Potter) –! Character Detection –! Topic Detection –! Emotion Detection –! Story Complexity –! Book Features –! Readability Characters (Part of Stephen King’s “It”) Character are, for example, (1) Named Entities (2) often agents of verbs (3) usually not after prepositions indicating a location Age Suitability Analysis 8
  • 9. Outline •! Visual Analytics –! Motivation and Definition –! Visualization for the e-Humanities •! Visual Analytics Examples –! Literature Analysis –! Language Analysis –! Political Analysis •! Perspectives Cross-Language Analysis 9
  • 10. Cross-Language Analysis Languages from Papua New Guinea with leaves showing features ordered to maximize (left) and minimize (right) the pairwise leaf similarity Cross-Language Analysis 10
  • 11. Vowel Harmony: Cross-linguistic Comparison of Complex Language Features “two-level” Vowel Harmony i and u avoid each other “one-level” Vowel Harmony syllable reduplication Vowel succession patterns in 42 languages (automatically sorted by significance) [2] Vowel Harmony: Cross-linguistic Comparison of Complex Language Features Comparing Swedish and Norwegian: Vowel transitions according to their position within words based on at least 50 Bible types. Vowel transitions according to their position within words. Only those transitions plotted based on at least 200 Bible types (interactive filter). 11
  • 12. Tracking Semantic Change Frequency development of different word senses automatically induced from word contexts with topic modeling. Data: NYT Annotated Corpus, 1.8 million articles from daily newspaper editions 1987-2007 Reprinted from [3], © 2011 Association for Computational Linguistics Analyzing Prosodic Features: Intonation 12
  • 13. Analyzing Prosodic Features: Intonation Outline •! Visual Analytics –! Motivation and Definition –! Visualization for the e-Humanities •! Visual Analytics Examples –! Literature Analysis –! Language Analysis –! Political Analysis •! Perspectives 13
  • 14. One day of the Stuttgart 21 mediations BMBF Project VisArgue Presidential Debate Analysis BMBF Project VisArgue 14
  • 16. Comparison of US-Presidential Debates Obama vs. McCain 2008 Obama vs. Romney 2012 BMBF Project VisArgue Stuttgart 21 Discourse Analysis BMBF Project VisArgue 16
  • 17. Analysis of Policy Networks Parallel Tag Clouds to Show Differences across US Court Circuits Reprinted from Collins et al. [9], © 2009 IEEE 17
  • 18. Voronoi Treemaps [10] in NYT http://www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html?_r=0 Outline •! Visual Analytics –! Motivation and Definition –! Visualization for the e-Humanities •! Visual Analytics Examples –! Literature Analysis –! Language Analysis –! Political Analysis •! Perspectives 18
  • 19. Visualization in the Digital Humanities •! Visualization is central to allow humans and computers to cooperate effectively –! allow the computer to process large data –! allow the human to understand and interact with large data •! Interactive Visualization is central for –! Exploration of Data –! Interpretation of Results –! Generation of Hypotheses –! Steering of the Analysis 19
  • 20. Thank you for your attention. Questions? “Anyone who claims to know all the answers doesn't really know very much.” Apostle Paul in 1. Cor. 8,2 infovis.uni-konstanz.de 20