Green Blue Blue Blue Red Red Text visualization Why use text - PowerPoint PPT Presentation

Web-pages Email Green Blue Blue Blue Red Red Text visualization Why use text in visualization? Instant messages Digitized books, articles Lucas Rizoli CPSC 533C, November 2006 2 3 4 Fast Text can be a dense representation New York Justice Five Reading is fast Dense Text can be inexact Inexact [from http://en.wikipedia.org/wiki/Image:Statue-Of-Liberty.jpg] 5 6 7 8 Space Arrangement Difficulties of using text Orientation Legibility Meaning [from http://www.futureofthebook.org/mitchellstephens/] [from http://www.textarc.org/] 9 10 11 12 Index Searching Explicit in data [from http://enron.trampolinesystems.com/] [from http://jheer.org/enron/] [from http://www.textarc.org/] 13 14 15 16

Graph Analyzing Reliance on meta-data Derived from data Says little about content Human supervision of automated processes [from http://jheer.org/enron/] [from http://www.idlewords.com/2004/03/your_literary_masterpiece_was_delicious.htm] 17 18 19 20 Wordiness CAPS Direction of conversation Exclamations [from Tat, A., & Carpendale, M. S. T. (2002)] [from Tat, A., & Carpendale, M. S. T. (2002)] [from Tat, A., & Carpendale, M. S. T. (2002)] [from Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002)] 21 22 23 24 Unique visual representation Exploration Derived from data Increasingly semantic Greater reliance on human users [from Viégas, F. B., Golder, S., & Donath, J. (2006)] [from http://alumni.media.mit.edu/~fviegas/projects/themail/study/index.htm] [from Viégas, F. B., Golder, S., & Donath, J. (2006)] 25 26 27 28 Trouble pre-processing data Adjusting for word frequency Finding meaning in text is difficult Take-home lessons Many assumptions made Full semantic processing 29 30 31 32

● Ceglowski, M. (2004). Your Literary Masterpiece Was Delicious . Retrieved November 6, 2006 from http://www.idlewords.com/2004/03/your_literary_masterpiece_was_delicious.htm ● Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002). ThemeRiver: Visualizing Thematic Changes in Large Document Collections. Visualization and Computer Graphics, IEEE Visualizing text Transactions , 8, 9-20. Text in visualization ● Heer, J. (2004). Exploring Enron: Visualizing ANLP Results . Retrieved November 6, 2006 from http://jheer.org/enron/v1/ ● Paley, W. B. (2002). TextArc: Showing Word Frequency and Distribution in Text. In Wong, P. C., Range of levels and methods & Keith Andrews (Eds.), Proceedings of the IEEE Symposium on Information Visualization (Infovis '02) Poster Compendium . Los Alamitos, CA, USA: IEEE Press. Fast, dense, inexact ● Paley, W. B. (n.d.). TextArc.org Home . Retrieved November 6, 2006 from http://textarc.org/ Meta-data adds structure Complicated to apply ● Tat, A., & Carpendale, M. S. T. (2002). Visualizing Human Dialog. Proceedings of the IEEE Conference on Information Visualization (Infovis '02) . London, UK: IEEE Press. Pre-processing is hard, important ● Trampoline Systems (n.d.). Trampoline Enron Explorer . Retrieved November 6, 2006 from http://enron.trampolinesystems.com/ ● Viégas, F. B., Golder, S., & Donath, J. (2006). Visualizing Email Content: Portraying Relationships from Conversational Histories. Proceedings of the SIGCHI conference on Human factors in computing systems (CHI '06) . Montréal, Québec, Canada: ACM Press. 33 34 35