1. Visualization Taxonomies and Techniques Text: Documents and Collections University of Texas – Pan American CSCI 6361, Spring 2014

2. Text: Documents and Collection Overview Visualizations for –Document sets –Words & sentences –Analysis metrics –Concepts and themes Recall, sensemaking –Gaining a better understanding of the facts at hand in order to take some next steps –(Better definitions in VA lecture) Information Visualization can help make a large document collection more understandable more rapidly

3. Overviews of Documents Document Cards Provide a quick browsing, overview UI, maybe especially useful for small screens Paper has useful detail about text processing Document Cards –Strobelt et al. TVCG (InfoVis) ‘09 –Compact visual representation of a document –Show key terms and important images

4. Document Cards Video without sound –http://www.informatik.uni-konstanz.de/en/deussen/publications/#c87338

5. Representation Layout algorithm searches for empty space rectangles to put things

6. Interaction Hover over non-image space shows abstract in tooltip Hover over image and see caption as tooltip Click on page number to get full page Click on image goes to page containing it Clicking on a term highlights it in overview and all tooltips

7. Interaction Hover over non-image space shows abstract in tooltip Hover over image and see caption as tooltip Click on page number to get full page Click on image goes to page containing it Clicking on a term highlights it in overview and all tooltips

8. Bohemian Bookshelf Video –http://vimeo.com/39034060

9. Bohemian Bookshelf Serendipitous browsing, Video, Thudt et al CHI ‘12

10. Themail Visualize one’s email history –With whom and when has a person corresponded –What words were used Answer questions like: –What sorts of things do I (the owner of the archive) talk about with each of my email contacts? –How do my email conversations with one person differ from those with other people?

11. Themail Text analysis to seed visualization Monthly & yearly words

12. Themail Query UI

13. PaperLens Video, but no sound

14. PaperLens Focus on academic papers Visualize doc metadata such as author, keywords, date, … Multiple tightly-coupled views Analytics questions Effective in answering questions regarding: –Patterns such as frequency of authors and papers cited –Themes –Trends such as number of papers published in a topic area over time –Correlations between authors, topics and citations

15. PaperLens a) Popularity of topic b) Selected authors c) Author list d) Degrees of separation of links e) Paper list f) Year-by-year top ten cited papers/ authors – can be sorted by topic

16. NetLens More Document Info Highlight entities within documents –People, places, organizations – Document summaries Document similarity and clustering Document sentiment

17. Jigsaw

18. Targeting sense-making scenarios –Visual analytics Variety of visualizations ranging from word-specific, to entity connections, to document clusters Primary focus is on entity-document and entity-entity connection Search capability coupled with interactive exploration –Stasko, Görg, & Liu Information Visualization ‘08 Will see video after Visual Analytics section

19. Jigsaw Document view

20. JigSaw List View Entities listed by type

21. JigSaw Document Cluster View Entities listed by type

22. Jigsaw Document Grid View Here showing sentiment analysis of docs

23. Jigsaw Video

24. Kohonen’s Feature Maps Organizing Document Collections AKA Self-Organizing Maps (SOMs) –Complex, non-linear relationships between high dimensional data items into simple geometric relationships on a 2-d display Uses neural network techniques –Lin Visualization ‘92 Different, colored areas correspond to different concepts in collection Size of area corresponds to its relative importance in set Neighboring regions indicate commonalities in concepts Dots in regions can represent documents

25. Kohonen’s Feature Maps Organizing Document Collections

28. Work at PNNL

29. Group has developed a number of visualization techniques for document collections –http://www.pnl.gov/infoviz –Galaxies –Themescapes –ThemeRiver –...

30. Galaxies Presentation of documents where similar ones cluster together

31. Themescapes Self-organizing maps didn’t reflect density of regions all that well Use 3D representation, and have height represent density or number of documents in region

32. WebTheme

33. Spire Video

34. Maps of Science –http://scimaps.orghttp://scimaps.org Visualize the relationships of areas of science, emerging research disciplines, the impact of particular researchers or institutions, etc. Often use documents as the “input data”

35. Maps of Science Book and web site http://scimaps.org

36. Map of Science http://scimaps.org/maps/map/map_of_scientific_pa_55/

37. Map of Science http://scimaps.org/maps/map/maps_of_science_fore_50/

38. Map of Science Science Related Wikipedia Activity –http://scimaps.org/maps/map/science_related_wiki_49/http://scimaps.org/maps/map/science_related_wiki_49/

39. Temporal Issues Semantic map gives no indication of the chronology of documents Can we show themes and how they rise or fall over time? ThemeRiver

40. Time flows from left->right Each band/current is a topic or theme Width of band is “strength” of that topic in documents at that time

41. Topic Modeling High interest topic in text analysis and visualization Latent Dirichlet Allocation Unsupervised learning Produces “topics” evident throughout doc collection, each modeled by sets of words/terms Describes how each document contributes to each topic

42. TIARA Keeps basic ThemeRiver metaphor –Liu et al CIKM ‘09, KDD ‘10, VAST ‘10 Embed word clouds into bands to tell more about what is in each Magnifier lens for getting more details Uses Latent Dirichlet Allocation to do text analysis and summarization

43. TIARA

44. Tiara Features Lens shows email senders & receivers Documents containing “cotable”

45. TextFlow Showing how topics merge and split –Cui et al TVCG (InfoVis) ‘11

46. ParallelTopics Dou et al VAST ‘11

47. End.

48. Sources Stasko, J. (2013). CS 7450 - Information Visualization at Georgia Tech Hearst, M. (2009) Search User Interfaces http://www.searchuserinterfaces.com/http://www.searchuserinterfaces.com/ –Ch. 10: Information Visualization for Search Interfaces: 10.3, 10.4, 10.9, 10.10; http://searchuserinterfaces.com/book/sui_ch10_visualization.html http://searchuserinterfaces.com/book/sui_ch10_visualization.html –Ch. 11: Information Visualization for Text Analysis; http://searchuserinterfaces.com/book/sui_ch11_text_analysis_visualization.html http://searchuserinterfaces.com/book/sui_ch11_text_analysis_visualization.html F. Viegas, M. Wattenberg, "Tag Clouds and the Case for Vernacular Visualization", interactions, Vol. 15, No. 4, Jul-Aug 2008, pp. 49-52.

49. Video Resources TileBars (file) 5+ min “document cards” video (vimeo) – no sound Parallel Tag Clouds http://www.youtube.com/watch?v=rL3Ga6xBgLw (long)http://www.youtube.com/watch?v=rL3Ga6xBgLw FeatureLens http://www.cs.umd.edu/hcil/textvis/featurelens/ (long – find spots)http://www.cs.umd.edu/hcil/textvis/featurelens/ FacetAtlas http://vimeo.com/76069684 - 17 minuteshttp://vimeo.com/76069684 http://in-spire.pnnl.gov/ - 3:50http://in-spire.pnnl.gov/ –Galaxy, etc. Jigsaw (file) – VISUAL ANALYTICS EXAMPLE PaperLens (file) FeatureLens (file) Mani-Wordle - Koh et al TVCG (InfoVis) ‘10 -- video