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Text Visualization

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  1. Text Visualization Marti Hearst Guest Lecture, i247, Spring 2012

  2. Graphing Quantitative Data

  3. Graphing Quantitative Data

  4. Graphing Nominal Data

  5. Preattentive Properties

  6. Text is NOT Preattentive SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC

  7. Text As Notation

  8. Notation

  9. Logograms

  10. Syllabary

  11. Syllabary

  12. Alphabet

  13. Alphabet

  14. So…. How Do We Visually Represent Content?

  15. “It appeared that the narrative he had promised to read us really required for a proper intelligence a few words of prologue. Let me say here distinctly, to have done with it, that this narrative, from an exact transcript of my own made much later, is what I shall presently give. Poor Douglas, before his death—when it was in sight—committed to me the manuscript that reached him on the third of these days and that, on the same spot, with immense effect, he began to read to our hushed little circle on the night of the fourth.” Henry James, Turn of the Screw

  16. Movies? Georges Méliès Le Voyage dans la Lune (A Trip to the Moon) (1902)

  17. Graphic Novel

  18. Visualization in Search

  19. A Comparative Study • Reiterer et al., SIGIR 2000 • Well-done study • They weren’t the creators of the viz’s tested • 40 participants, varied tasks • Compared: • Plain html web page • Sortable search results (in a table view) • Tilebars-like view • Bar charts view • Scatterplot view

  20. A Comparative Study • Reiterer et al., SIGIR 2000 • Results: • People weren’t any better with viz’s than with standard web view. Significantly worse with bar charts • Subjective results: Sortable Table, then Tilebars, then simple web-based view • People disliked the bar charts and scatter plots

  21. Starfield (Clustering-based)VisualizationsWise et al 95

  22. Starfield (Clustering-based) Visualizations

  23. Starfield (Clustering-based) VisualizationsWise et al 95

  24. Starfield (Clustering-based) Visualizations

  25. Are visual clusters useful? • Four Clustering Visualization Usability Studies • Conclusions: • Huge 2D maps may be inappropriate focus for information retrieval • cannot see what the documents are about • space is difficult to browse for IR purposes • (tough to visualize abstract concepts) • Perhaps more suited for pattern discovery and gist-like overviews.

  26. Clustering Algorithm Problems • Doesn’t work well if data is too homogenous or too heterogeneous • Often is difficult to interpret quickly • Automatically generated labels are unintuitive and occur at different levels of description • Often the top-level can be ok, but the subsequent levels are very poor • Need a better way to handle items that fall into more than one cluster

  27. Creative Facet Visualization • Aduna Autofocus

  28. Creative Facet Visualization • We Feel Fine

  29. Creative Facet Visualization • Fathumb mobile search interface • http://research.microsoft.com/vibe/projects/FaThumb.aspx

  30. Creative Facet Visualization • Hutchinson et al.

  31. Visualization in Text Analysis

  32. Hearst & Rosner, 2007 What’s Up With Tag Clouds?

  33. Definition Tag Cloud: A visual representation of social tags, organized into paragraph-style layout, usually in alphabetical order, where the relative size and weight of the font for each tag corresponds to the relative frequency of its use.

  34. Definition Tag Cloud: A visual representation of social tags, organized into paragraph-style layout, usually in alphabeticalorder, where the relative size and weightofthefont for each tag correspondsto the relative frequency of its use.

  35. flickr’s tag cloud

  36. del.icio.us

  37. del.icio.us

  38. blogs

  39. ma.gnolia.com

  40. IBM’s manyeyes project