1. Visual Computing Text Visualization Based on slides by Chris North, Virginia Tech Jeffrey Heer, Stanford University

2. Text & Document Visualization Text not pre-attentive Text = Abstract Concepts = Very High Dimensionality –Multiple & ambiguous meanings –Combinations of abstract concepts more difficult to visualize –Different combinations imply different meanings –Language only hints at meaning  based on common understanding “How much is that doggy in the window?” Facilitate Information Retrieval –Collection Overview –Visualize which parts of query satisfied by document / collection –Understand why documents retrieved Cluster Documents Based on Words in Common –Finds overall similarities among groups of documents –Picks out some themes, ignores others Map Clusters onto 2D or 3D Representation –Minimize time/effort to decide which documents to examine

3. What is text data? Documents Articles, books and novels Computer programs E-mails, web pages, blogs Tags, comments Collection of documents Messages (e-mail, blogs, tags, comments) Social networks (personal profiles) Academic collaborations (publications)

4. Text as Data Words are (not) nominal? High dimensional (10,000+) More than equality tests Words have meanings and relations –Correlations: Hong Kong, San Francisco, Bay Area –Order: April, February, January, June, March, May –Membership: Tennis, Running, Swimming, Hiking, Piano –Hierarchy, antonyms & synonyms, entities, …

5. Text Processing Pipeline Tokenization: segment text into terms –Special cases? e.g., “San Francisco”, “L’ensemble”, “U.S.A.” –Remove stop words? e.g., “a”, “an”, “the”, “to”, “be”? Stemming: one means of normalizing terms –Reduce terms to their “root”; Porter’s algorithm for English –e.g., automate(s), automatic, automation all map to automat –For visualization, want to reverse stemming for labels –Simple solution: map from stem to the most frequent word Result: ordered stream of terms

6. The Bag of Words Model Ignore ordering relationships within the text A document ≈ vector of term weights –Each dimension corresponds to a term (10,000+) –Each value represents the relevance –For example, simple term counts Aggregate into a document x term matrix –Document vector space model

7. Document x Term matrix Each document is a vector of term weights Simplest weighting is to just count occurrences Antony and Cleopatra Julius Caesar The Tempest HamletOthelloMacbeth Antony157730000 Brutus41570100 Caesar2322270211 Calpurnia 0100000 Cleopatra5700000 mercy203551 worser201110

8. WordCount (Harris 2004) http://wordcount.org WordCount™ is an interactive presentation of the 86,800 most frequently used English words.

9. Term Vector Theory for Information Retrieval (IR) Vector Space Model IR systems assign weights to terms by considering 1.local information from individual documents 2.global information from collection of documents Systems that assign weights to links use Web graph information to properly account for the degree of connectivity between documents. In IR studies, the classic weighting scheme is the Salton Vector Space Model, commonly known as the "term vector model". This weighting scheme is given by Term Weight = where tf i = term frequency (term counts) or number of times a term i occurs in a document. df i = document frequency or number of documents containing term i D = number of documents in the database. Many models that extract term vectors from documents and queries are derived this equation.

10. Computing Weights Term Frequency t = term were are searching for tf td = count(t) in d df t = # docs containing t N = # of docs TF.IDF: Term Freq by Inverse Document Freq tf.idf td = tf td × log(N/df t ) This is the relative importance in the document Word is more important in the fewer document it appears. — We are more interested in a words that appear often in a single document not in the collection as a whole

11. Term vectors for Group of Docs with tf-idf weights

12. Visualizing Document Content

13. Tag Cloud: Word Counts

14. Wordle During the campaign, Palin gave an energetic speech in Dayton, Ohio. She appealed to women voters by evoking, of all things, the presidential campaign of Democrat Hillary Clinton, saying, “Hillary left 18 million cracks in the highest, hardest glass ceiling in America. But it turns out the women of America aren’t done yet.” Here’s that speech: Text from: California WatchCalifornia Watch http://www.wordle.net/create

15. Weaknesses of Tag Clouds Sub-optimal visual encoding (size vs. position) Inaccurate size encoding (long words are bigger) May not facilitate comparison (unstable layout) Term frequency may not be meaningful Does not show the structure of the text

16. Word Tree: Word Sequences

17. TextArc – Brad Paley http://textarc.org/

18. Arc Diagrams – M. Wattenberg Les Misérables character interaction. Each character is represented by a circle and the connecting arc represents co-occurrence in a chapter. The character's size indicates the number of appearances they have over the entire work.

19. Literature Fingerprinting Problem: Authorship Attribution Determine, if a text was written by an author or not. A common problem in literary analysis. What features are useful for discrimination? Case study on some books by Jack London and Mark Twain.

20. Variables for Literary Analysis Statistical measures – Syllables per word – Sentence length – Proportions of parts of speech –... Vocabulary measures – Frequencies of specific words – Type-token ratio – Simpson’s index – Hapax (dis)legomena –... Syntax measures

21. Average Sentence Length

22. Structured Document Collections Multi-dimensional: author, title, date, journal, … Trees: Dewey decimal system Graphs: web, citations

23. Citation Networks ANNOTATIONS TYPESCRIPT select source | ?b 434 | search on author | ?s au=card sk | search S1 had 7 results | S1 7 AU=CARD SK type S1 format 3 result 1 | ?t 1/3/1 | | 1/3/1 | DIALOG(R)File 434:Scisearch(R) | (C) 1994 Inst For Sci Info. All Rts. Reserv. | | 12204937 Genuine Article#: KU797 No. Reference... | Title: INFORMATION VISUALIZATION USING 3D INTERAC... first author | Author(S): ROBERTSON GG; CARD SK; MACKINLAY JD | Corporate Source: XEROX CORP,PALO ALTO RES CTR,33... | ALTO//CA/94304 year, vol, page | Journal: COMMUNICATIONS OF THE ACM, 1993, V36, N4... | ISSN: 0001-0782 | Language: ENGLISH Document Type: ARTICLE search for citers | ?s cr=robertson gg, 1993, v36, p56, ? | search S2 had 1 result | S2 1 CR=ROBERTSON GG, 1993, V36, P56, ? This annotated typescript from a DIALOG session shows a search of the Science Citation Database for articles that include S. K. Card as an author. Typescripts like this do not particularly show the structure of a search.

24. Butterfly Browser - Mackinlay et al (PARC) Based on four key ideas: Visualizations Of References And Citers –Visualize scholarly articles as user interface objects with two wings, one wing for listing an article's references and the other wing for listing the article's citers. Link-Generating Queries –Automatically create link-generating queries that link an article's record to the corresponding records for the article's references and citers Asynchronous Query Processes –Uses asynchronous processing for information access so the user does not have to wait for queries to complete Embedded Process Control –User can explicitly create and terminate query processes

25. Butterfly Browser Butterfly: Left = refs Right = citers Yellow = #citers Blue = visited 3d plot: date, Name, # citers

27. Unstructured Document Collections Focus on Full Text Examples: digital libraries, news archives, web pages email archives, image galery Tasks: Search Browse Classification, structurization Statistics, keyword usage, languages Subjects, themes, coverage

28. Visualization Strategies Cluster Maps Keyword Query results Relationships Reduced representation User controlled layout

29. Cluster Map Create a “map” of the document collection Similar documents near each other Dissimilar documents far apart “Library” or “Grocery store” concept

30. Document Vectors Doc1Doc2Doc3 … “aardvark”120 “banana”210 “chris”003 … Now it’s a Multi-D visualization problem? Dimensionality reduction: Projection: e.g. Principal Components Analysis (PCA) Similarity-based methods: 1. Compute “Similarity” between pair of docs 2. Layout documents in 1/2/3-D map by similarity

31. Similarity Matrix Doc1Doc2Doc3 … “aardvark”120 “banana”210 “chris”003 … Similarity metrics? dot product Doc1Doc2Doc3… Doc110.660 Doc20.6610 Doc3001 …

32. Layout Mapping Spring model of graph layout Multi-Dimensional Scaling (MDS) Self-organizing Map (kohonen map) Clustering: Partition, hierarchical How to label a group? …

33. Cluster Algorithms Partition clustering: Partition into k subsets Pick k seeds Iteratively attract nearest neighbor Hierarchical clustering: Dendrogram Group nearest-neighbor pair Iterate Top down Bottom up

34. Landscapes Wise et al, “Visualizing the non-visual” ThemeScapes, Cartia, IN-SPIRE (PNNL) Mountain = topical theme Mountain height = number of relevant documents

35. LandScapes Abstract, 3D landscapes of information Convey relevant information about topic or themes without the cognitive load Spatial relationships reveal the intricate interconnection of thems Dominant themes are shown in a relief map of natural terrain. Themes are represented by peaks and their height indicates relative strength within the document set.

36. Advantages Displays much of the complex content of the document database Utilizes innate human abilities for pattern recognition and spatial reasoning Communicative invariance across levels of textual scale Promotes analysis

37. ThemeRiver - PNNL Displays changes to themes over time Helps users identify time-related patterns, trends, and relationships across a large collection of documents. Themes in the collection are represented by a "river" that flows left to right through time. The river widens or narrows to depict changes in the collective strength of selected themes in the underlying documents. Individual themes are represented as colored "currents" flowing within the river. The theme currents narrow or widen to indicate changes in individual theme strength at any point in time.

38. ThemeRiver http://infoviz.pnl.gov/images/ThemeRiver.mov

39. Galaxies Displays cluster and document interrelatedness 2D scatterplot of ‘ docupoints ’ Simple point and click exploration Sophisticated tools –Facilitate more in-depth analysis –Ex) temporal slicer

40. IN-SPIRE™ - PNNL The Galaxy visualization uses the metaphor of stars in the night sky where each star represents a document. Closely related documents cluster together while unrelated documents are further apart. Galaxies help users to understand what is in a document collection and allows them to explore the context of their specific interests.

41. GalaxyView Dot = document Galaxy = cluster

42. StarLight - PNNL Relationships to geography, etc.

43. The Self-Organizing Map (SOM) Data visualization technique invented by Teuvo Kohonen which reduces the dimensions of data through the use of self- organizing neural networks. SOMs reduce dimensions by producing a map of 1 or 2 dimensions that plots the similarities of the data by grouping similar data items together.

44. Components of SOM 1.Sample Data –e.g. RGB (3 dimensions) 2.Weight vectors –two components: The data itself The data’s natural location –e.g. 2D array of weight vectors (say, colors at right)

45. Components of SOM Algorithm Initialize Map For t from 0 to 1 Randomly select a sample Get best matching unit Scale neighbors Increase t a small amount End for

46. Self-organizing Maps Xia Lin, “Document Space” Kohonen map, http://faculty.cis.drexel.edu/sitemap/index.htmlhttp://faculty.cis.drexel.edu/sitemap/index.html