Your SlideShare is downloading. ×
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Digital humanitiesherrenhaeusserforum2013keim
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Digital humanitiesherrenhaeusserforum2013keim

255

Published on

Talk at Digital Humanities Conference at Herrenhaeusser Forum December 2013

Talk at Digital Humanities Conference at Herrenhaeusser Forum December 2013

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
255
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
6
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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
  • 15. Presidential Debate Analysis Topic Shifts BMBF Project VisArgue Presidential Debate Analysis Crosstalk BMBF Project VisArgue 15
  • 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

×