Last Week: Visualization Design II

Transcription

1 Last Week: Visualization Design II Chart Junks Vis Lies 1

2 Last Week: Visualization Design II Sensory representation Understand without learning Sensory immediacy Cross-cultural validity Arbitrary representation Hard to learn Easy to forget Embedded in culture and apps 汉字 : 一二三人 dog 山 Antidisestablishmentarianism 森 Euler diagram: circle for boundary Language: need to learn 2

3 Last Week: Data Model and Explorative Visual Analytics 1-D (Linear, Set and Sequences) SeeSoft, Info Mural 2-D (Map) GIS, ArcView, PageMaker 3-D (Shape, the World) CAD, Medical, Architecture n-d (Relational) Spotfire, Tableau Temporal LifeLines, Palantir Tree (Hierarchy) Cone/Cam/Hyperbolic Network (Graph) Pajek, JUNG 3

4 Last Week: Data Model and Explorative Visual Analytics 4

5 Text Visualization I IV Course Spring 14 Graduate Course of UCAS Mar. 28th,

6 InfoVis Pipeline Visualization Text Visualization Data Text Data Model User Potential Users? Tasks 6

7 Outline Text visualization background Examples User, tasks and text visualization pipeline Text visualization approaches Information Retrieval purpose Overview and sense-making purpose Text analytics basics Word/sentence-level Corpus-level 7

8 Text is Everywhere We use documents as primary information artifact in our lives Our access to documents has grown tremendously in recent years due to the Internet... WWW Digital libraries Web 2.0 8

9 Text Visualization Examples 9

10 Examples 10

11 Examples 11

12 Examples 12

13 Examples 13

14 Examples 14

15 Examples 15

16 Examples 16

17 Examples 17

18 Big gquestions What can information visualization provide to help users in understanding and gathering information from text and document collections? (Task) Who will be interested and benefit from text visualization? (User)... 18

19 Tasks & Goals Which h documents contain text t on topic XYZ? Which documents are of interest to me? Are there other documents that are similar to this one (so they are worthwhile)? How are different words used in a document or a document collection? What are the main themes and ideas in a document or a collection? Which documents have an angry tone? How are certain words or themes distributed through a document? Identify hidden messages or stories in this document collection. How does one set of documents differ from another set? Quickly gain an understanding of a document or collection in order to subsequently do XYZ. Understand the history of changes in a document. Find connections between documents. 19

20 Another Task: Ask Better Questions on Text Collections 20

21 Users of Text Visualization Government Intelligence Analysts? Literature researcher? Artist?...???? [To be answered in Assignment II] 21

22 Potential ti User: Parents y p 22

23 Text Visualization Pipeline 23

24 Text Visualization for Information Retrieval Which documents contain text on topic XYZ? Which documents are of interest to me? Are there other documents that are similar to this one (so they are worthwhile)?... 24

25 Text Visualization for Information Retrieval 25

26 TileBar Search engine query results do not include: How strong the match is How frequent each term is How each term is distributed in the document Overlap between terms Length of document Document ranking is opaque Inability to compare between results Input limits term relationships 26

27 TileBar Search Terms Query Result Visualization 27

28 TileBar 28

29 More Text Visualization for IR Visualize One query... query distance document 29

30 More Text Visualization for IR Multiple queries... 30

31 More Text Visualization for IR 31

32 Comparing Search Results Color represents different search engines 32

33 Text Visualization for Sensemaking How are different words used in a document or a document collection? What are the main themes and ideas in a document or a collection? on? Which documents have an angry tone? How are certain words or themes distributed through a document? Identify hidden messages or stories in this document collection. How does one set of documents differ from another set? Quickly gain an understanding of a document or collection in order to subsequently do XYZ. Understand the history of changes in a document. Find connections between documents

34 Text Visualization Method Taxonomy Document-level visualization: document distribution & summarization Text content-level visualization: overview & navigation Keyword frequency Associated facet: time, topic, sentiment, etc. Text entities in context: keyword occurrence Text entity relationship and/or internal text structure... 34

35 Document Visualization InfoSky & SPIRE: 2D projection of document vectors by PCA/MDS/etc. /... InfoSky SPIRE 35

36 Document Visualization Exemplar-based document visualization... Visualization of documents in 20 Newsgroups (18, documents, 20 topics) by EV. Each point represents a document; each color shape represents a news topic; and the corresponding big color shape indicates the mean of a news group. 36

37 Document Visualization Document Card InfoVis 08 Proceedings 37

38 Text Content Visualization: Keywords Bubble Chart 38

39 Text Content Visualization: Keywords Tag Cloud 39

40 Text Content Visualization: Keywords Ordered Tag Cloud 40

41 Text Content Visualization: Keywords Bi-gram 41

42 Text Content Visualization: Keywords Wordle 42

43 Text Content Visualization: Keywords Manipulating Wordle 43

44 Text Content Visualization with Facets TIARA & ThemeRiver & Context-Preserving Tag Cloud & Parallel TagCloud Temporal/topical/facet extension of TagCloud/Wordle Provide more interactions to drill-down to small document portions TIARA ThemeRiver Context-Preserving TagCloud Parallel TagCloud 44

45 Text Content Visualization with Facets Parallel Tag Cloud 45

46 Text Entities: Keyword in Context TAKMI & FeatureLens & TileBar Visualizing entity/feature/concept within the content Visualizing occurrence patterns within the content: temporal, topical, correlational Keyword + context paradigm for details FeatureLens TileBar TAKMI 46

47 Visual Readability Analysis 47

48 Jigsaw & WordTree Visualizing entity relationships Text Entity Relationship Extract natural relationships: co- occurrence, sequential Support navigation with focus redirection Jigsaw Word Tree 48

49 Text Entity Relationship PhraseNet & FacetAtlas Visualizing entity relationships with advanced analytics: WordNet, intermediate word, multi-faceted relationships Start t from a search item : relationship item or concept item Only visualization, few navigation PhraseNet DocuBurst FacetAtlas 49

50 Text Analytics Basics: Text Mining Text pre-processing (parsing) Remove stop words Keyword stemming Text feature extraction Keyword frequency Topic modeling Text feature measurement m Similarity Text clustering 50

51 Text Parsing "I have a dream that one day this nation will rise up and live out the true meaning of its creed: "We hold these truths to be self- evident, that t all men are created equal." Stop word removal: a, the, that, t etc. Keyword stemming: men->man, truths->truth Parsing result: I, dream, one, day, nation, rise, up, live, out, true, meaning, creed, hold, truth, be, self-evident, all, man, created, equal 51

52 Basic Text Modeling Bag-of-words model: vector representation Word I dream color skin nation slave injustice owner Frequency Text similarity:cosine similarity between two words TF-IDF weighting: term frequency * inverse document frequency 52

53 Topic Modeling Popular methods: Latent Semantic Indexing plsi, LDA 53

54 Background Examples Summary User, tasks and text visualization pipeline pp Text visualization methods IR purpose Overview and sense-making: 5 categories Text analytics basics Text parsing, measurement and topic modeling 54

55 Questions? What s Next -- Lecture 8: Text Visualization II 55