1/41 Visualization and Analysis of Text Remco Chang, PhD Assistant Professor Department of Computer Science Tufts University December 17, 2010 Cologne, Germany

2/41 CMVVisExamplesP Topics Introduction Information Visualization – Novel visual representations – Storytelling – User-Driven Visual Analysis – Data exploration – Hypotheses generation – Interactive visualization + Computation

3/41 CMVVisExamplesP Topics Visualization Pre-attentive Processing Examples courtesy of Chris Healey

4/41 CMVVisExamplesP Topics Visualization This is helpful because: – It allows us to process more information quickly – We can see trends and patterns

5/41 CMVVisExamplesP Topics Storytelling US Budget from

6/41 CMVVisExamplesP Topics Storytelling Minard’s Map: Napolean’s March to Moscow

7/41 CMVVisExamplesP Topics Visualization Influences the thought… Images courtesy of Barbara Tversky

8/41 CMVVisExamplesP Topics Visual Encoding Affects the: – Types of possible operations – The user’s thinking process Zhang and Norman. The Representation Of Numbers. Cognition. (1995)

9/41 CMVVisExamplesP Topics Classifying Numeric Systems

10/41 CMVVisExamplesP Topics Example: Arithmetic Slide courtesy of Pat Hanrahan

11/41 CMVVisExamplesP Topics Example: Arithmetic

12/41 CMVVisExamplesP Topics Example: Arithmetic

13/41 CMVVisExamplesP Topics Example: Arithmetic

14/41 CMVVisExamplesP Topics Examples of Text Visualization Wordle Images Courtesy of Many Eyes

15/41 CMVVisExamplesP Topics Examples of Text Visualization WordTree

16/41 CMVVisExamplesP Topics Examples of Text Visualization WordTree

17/41 CMVVisExamplesP Topics Examples of Text Visualization Phrase Net

18/41 CMVVisExamplesP Topics Examples of Text Visualization Google Auto- Complete

19/41 CMVVisExamplesP Topics Examples of Text Visualization Visualizing changes in Wikipedia Images Courtesy of Info.fm

20/41 CMVVisExamplesP Topics Examples of Text Visualization ThemeRiver 20

21/41 CMVVisExamplesP Topics Visual Exploration Coordinated Multi-Views (CMV) Where When Who What Original Data Evidence Box

22/41 CMVVisExamplesP Topics WHY ? WHY ? This group’s attacks are not bounded by geo-locations but instead, religious beliefs. Its attack patterns changed with its developments. Coordinated Multi-Views

23/41 CMVVisExamplesP Topics LIDAR Linked Feature Space 23/37

24/41 CMVVisExamplesP Topics LIDAR Change Detection 24/37

25/41 CMVVisExamplesP Topics Urban Model 25/37

26/41 CMVVisExamplesP Topics Urban Visualization 26/37

27/41 CMVVisExamplesP Topics Coordinated Multi-Views Financial Wire Fraud – With Bank of America – Discover suspicious international wire transactions Bridge Maintenance – With US DOT – Exploring subjective inspection reports Biomechanical Motion – With U. Minnesota and Brown – Interactive motion comparison methods

28/41 CMVVisExamplesP Topics Coordinated Multi-Views Financial Wire Fraud – With Bank of America – Discover suspicious international wire transactions Bridge Maintenance – With US DOT – Exploring subjective inspection reports Biomechanical Motion – With U. Minnesota and Brown – Interactive motion comparison methods

29/41 CMVVisExamplesP Topics Coordinated Multi-Views Financial Wire Fraud – With Bank of America – Discover suspicious international wire transactions Bridge Maintenance – With US DOT – Exploring subjective inspection reports Biomechanical Motion – With U. Minnesota and Brown – Interactive motion comparison methods

30/41 CMVVisExamplesP Topics CMV + Text Analysis

31/41 CMVVisExamplesP Topics Parallel Topics Task: Given the proposals submitted to the National Science Foundation (NSF), identify: – Proposals that are interdisciplinary – Proposals that are potentially transformative – Proposals that are focused

32/41 CMVVisExamplesP Topics Parallel Topics Approach: – Apply topic modeling algorithms to identify latent topics (David Blei, “Latent dirichlet allocation”, 2003) – Visualize the distribution of proposals based on the topics

33/41 CMVVisExamplesP Topics Topic Modeling Given a set of k documents, find n number of topics – Each document then is described as: (W 1 * Topic 1, W 2 * Topic 2, W 3 * Topic 3, …, W n * Topic n ) W 1 + W 2 + W 3 + … + W n = 1 Topic 1Topic 2…Topic N Document …0.005 Document …0.01 … Document K ∑ =

34/41 CMVVisExamplesP Topics Topic Modeling A topic is a combination of keywords

35/41 CMVVisExamplesP Topics Parallel Topics Based on “Parallel Coordinates” – Each vertical axis is a topic – Each set of horizontal connected lines is a document

36/41 CMVVisExamplesP Topics Visual Signatures Single topicBi-topic No salient topic We identify different signatures for proposals: – Single Topic – focused research – Bi-Topic – Interdisciplinary research – No-Topic – Potentially transformative research

37/41 CMVVisExamplesP Topics Selecting Single Topic Proposals Topic 1Topic 2…Topic N Document …0.005 Document …0.01 … Document K SD = 0.14 SD = 0.06 Max SD

38/41 CMVVisExamplesP Topics Selecting Multi-Topic Proposals education technology Interactive environment

39/41 CMVVisExamplesP Topics Selecting No-Topic Proposals

40/41 CMVVisExamplesP Topics Recap Objective: To discover interdisciplinary and potentially innovative research proposals Parallel Topics – data-centric approach Approach: To support interactive selection of proposals based on their number of topics

41/41 CMVVisExamplesP Topics Questions and Comments? Thank you!!