Interactive Visual Text
Analytics for Decision Making
Shixia Liu

Microsoft Research Asia
1

Text is Everywhere We use documents as primary information
artifact in our lives
 Our access to documents has growntremendously in recent years due to
networking infrastructure– WWW– Digital libraries–…

2

Big Question What can information visualization provide tohelp users in understanding and gathering
information from text and document collections?

3

Outline Example tasks in text analytics
 Visually analyzing textual information– Dynamic word cloud– Topic-based visual text summarization– TextFlow: towards better understanding of evolving
topics in text

 Future work4

Text Analytics: Our
Understanding
How can I find information buried inside the piles of text?

Information finding
5

Text Analytics: Our
Understanding
What is in my text?
What’s inside theNHTSA Data:

What are the major
causes of injuries• 450,000+documents• 70,000+ patient
emergency room records

What did mycustomers say about
my hotels• 3000+ customerposted reviews

Information Understanding: Text Summarization
6

Text Analytics: Our
Understanding
What is in my text?
Which hotel features do How customers’
my customers
sentiment havelike/dislikechanged toward myhotels• 3000+ customer reviews• 3000+ customerposted reviews

How do customers
feel about my new
product launch• thousands of eopinion postings

Insight Discovery: Sentiment Analysis
7Text Analytics: Our Understanding
What is in my text?
What are thecorrelations oftire problems andhighway death in
the NHTSA Data:• 450,000+
documents

What are thecorrelations ofpatient gender andthe cause of injury• 70,000+ patient
emergency room
records

Compare thecustomers’
attitude toward
our product with
theirs for ourcompetitors• thousands of eopinion postings

Decision Making and Problem
Solving: Text Analysis++8Major Challenges
 Huge amounts of complex information– Understanding the meanings of free text is just hard
– Performing analysis on top of that is harder Different people want different things– No one-size-fits-all solutions

 People may not know what they want
– “Tell me something I don’t know”– “I will tell you when I see it”

Machines are *not* just smart enough.
9Outline Example tasks in text analytics

 Visually analyzing textual information– Dynamic word cloud– TIARA: topic-based visual text summarization– TextFlow: towards better understanding of evolving
topics in text

 Future work10

Selected Projects
 Dynamic word cloud– Illustrate content evolution trend

 TIARA– Topic-based visual text summarization
and analysis

 TextFlow– Towards better understanding ofevolving topics in text

11

Cui et al. IEEE CG&A 2011

Dynamic Word Cloud Word clouds for content overview– Aesthetic issues– Inadequate for temporal patterns

 Standard time chart: trend– Inadequate for correlations

12

Our Solution
 A evolution trend chart + word clouds– Measure the evolution– Ensure the semantic coherence between clouds

13

Evolution Measurement
 Conditional entropy: measure the amount of
information contained by Xi but not by Xj

14

Word Cloud Layout
 Geometry meshes to ensure the semantic coherence– Semantically related words stay together– The same word in different clouds stay at the similar place15

Example: CG&A Abstracts

19892004

1994
2009

1999

1,984 abstracts IEEE Computer
Graphics and Applications (CG&A)
from 1981 to 200916

Comparison with Wordle

13,828 news articles
17Selected Projects
 Dynamic word cloud– Illustrate content evolution trend

 TIARA– Topic-based visual text summarization
and analysis

 TextFlow– Towards better understanding ofevolving topics in text

18

Demo
Y axis encodes topic
significance(3) Doc #(1) A layer
represents a topic(2) Topic
keywords

X axis encodes time

~10,000 emails in 200819

Demo20

Key Challenges
 Summarize text corpora– Huge amounts of complex information– Time-varying Visually explain summarization results– Consistent visualization

 Provide feedback or articulate their needs– Imperfect summarization results or varied user needs

21

TIARA Overview
Text collection

Text Preprocessing
Text content + meta data
Text Summarization
Summarization results
Data Transformation
Transformed results
Visualization
User
Interaction
22

TIARA Technical Focus
Text collection

Text Preprocessing
Text content + meta data
Text Summarization
Summarization results
Data Transformation
Transformed results
Visualization
UserInteraction23

Text Summarization
 Latent Dirichlet
Allocation (LDA)
model [Blei et al. 03]– High portability– High compaction rate forscalability– A finer grained modelkth document in
the collection{…, P(Ti | Dk), …}
A set of topic
probabilities{T1, …Ti,… TN }A set of topics

A set of keywords{W1, …, Wj, …, WM}
A set of word probabilities{…, P(Wj | Ti), …}
24

LDA Data Transformation
kth document in
the collection{…, P(Ti | Dk), …}
A set of topic
probabilities
A set of topics{T1, …Ti,… TN }Rank the topics to present
most interesting ones first
Select keyword sub-sets fortime-based summary{…} t-1, {…, Wj, …}t, {…} t+1,A set of keywords{W1, …, Wj, …, WM}
A set of word probabilities{…, P(Wj | Ti), …}25

[Song et al. CIKM 09]

Topic Ranking by User Interests
Domain-dependent
activeness metric Rank topics by “strength”
rank (Tk )  f  (Tk ) , (Tk ) , (Tk ) N ˆ
 (T )  N
Mkm 1Mm 1

m m, k

topiccoverage

topicvariance (T ) Mm 1kmN m ˆm , k   (Tk )Mm 1Nm

 Rank topics by “distinctiveness”
v~kT Lv~krank (Tk )  l (Tk )  ~ T ~v Dv
kgraph degreematrix

kgraph Laplacian

doc-topic
distribution



Tˆ
ˆ
ˆ
for each Tk, vk  1,k , 2,k ,..., M ,kv~k is normalized vk26

Experiments Goal– Measure which metric produces more “important” topics

 Data sets
– Email• 8326 email messages– News• 34,690 documents

 Method– Users indicate the importance of top-K ranked topics• Very important, somewhat important, Unimportant27

Results
 Email data (by F1 measure) News data (by F1 measure)28

TIARA Technical Focus
Text collection

Text Preprocessing
Text content + meta data
Text Summarization
Summarization results
Data Transformation
Transformed results
Visualization
UserInteraction
29

Visualizing Text: Existing Work Visualize text at ahigh level Visualize text at alow level Few on explaining
advanced text
analysis results

30Visual Text Summary Metaphor
Data to be visualized:
1. Topics: {T1, …Ti,… TN } and their probabilities
2. For each Ti ,Topic keywords by time: … {…,wik, …, }t, …
and their probabilities over time
3. For each Ti , Topic strength: {..., Si(t), …} over time
Visual encoding:Augmented stacked graph31

Enhanced Stacked Graph: Key Steps Computing geometry of layers
 Layer coloring Layer ordering Layer labeling32

Enhanced Stacked Graph: Key Steps Computing geometry of layers
 Layer coloring

Byron_Infovis08

 Layer ordering Layer labeling33

Layer Ordering Goals– Minimize distortion– Maximize usable space– Ensure semantic coherence

unorderedordered34

Layer Ordering - Comparison

Observed topic
35

Layer Ordering - Comparison

Same topic

36

Layer Ordering - Comparison

Same topic

37

Enhanced Stacked Graph: LayerLabeling Goals– Temporal proximity– Informativeness38

Enhanced Stacked Graph: LayerLabeling (cont’d) Our approach [Liu et al. CIKM09]– Constraint-based space allocation– Particle-based layout [Luboschik et al. 08] + wordles2

s1t-tt+

2t

T

39

Enhanced Stacked Graph: Layer Labeling (cont’d)40

Interacting with Visual Summary“people” involved and theirrelationships

41

Application Example: Healthcare Visualize text to facilitate analysis– Cause of injury– Reason for visit– Diagnosis

 Multiple fields of text data and their correlation
 Leverage structured data to help better
illustrate text information– Gender + Cause of injury

42

Correlation between Structured
and Text Fields

43

Correlation between Text Fields

Correlation between two fields,
diagnosis and reason for visit 44

Selected Projects
 Dynamic word cloud– Illustrate content evolution trend

 TIARA– Topic-based visual text summarization
and analysis

 TextFlow– Towards better understanding ofevolving topics in text45

Problems
Understanding topic evolution in large
text collections is important– Keep abreast of hot, new, and intertwining topics– Gain insight into the latent topics

46

Applications
 Scholars– Find related works in a publication set

 Business professional– Examine a large collection of emails and instantmessages Politicians– Examine online posts to identify the key public opinion
and concern

47

Example48

Example49

Application Example:933 VisWeek Pulications
geometry/rendering
flow/vector
volume/rendering
structure/layout
exploration/analytics

document/
temporal50

Application Example:
933 VisWeek Pulications

51

Challenges
 Model– Topic merging/splitting patterns

 Visually convey– Topic merging/splitting patterns in an intuitive way Facilitate– Analytical reasoning52

Related Work Most of the existing work
– Studying the evolution of individual topics

 Little work
– Studying topic merging and splitting patterns

 Barely been touched– Using visual analytics techniques to interactively analyze
complex topic evolution

53

Our Solution
 Leverage hierarchical Dirichlet processes– To model topic merging/splitting

 Augment familiar visual metaphors (rivers)– To convey the complex analytic results

 Support interactions at different levels– Smooth communication between visualization and the topic
mining model54

TextFlow Overview

55

Topic Data andRelationship Extraction
 Incremental Hierarchical Dirichlet Processes– Online learning of the topics in text
– Automatically detect the topic numbers– Extract the merging/splitting relationships• Based on document topic change• Online compute the merging/splitting probabilities

56

Splitting RelationshipTime tTime t+1
Predict

Refine57

Merging RelationshipTime tTime t+1
Predict

Refine58

Critical Event Extraction
 Types of critical events– Birth, death, merge, and split

 Scoring the merging/splitting event– Number of the branches– Entropy of the branching probabilities

59

Keyword Correlation Discovery
 Extract– noun phrases, verb phrases, and named entities in eachdocument

 Count– Co-occurrences among them

 Be used to illustrate “why”
60Topic Evolution as Flow
AlternativeOur design61Critical Event as Glyph Emerge, dissolve, split, and merge

62Keyword Correlation as Thread Alternatives

63Keyword Correlation as Thread Intertwine to indicate co-occurrences

64

Visualization Design - Consistency
Critical event
TopicKeyword

65Layout Algorithm Three-level directed acyclic graph (DAG)

66

Interactive Exploration

ilter

expandplaceglyphs
ilter

ilter

place
threadsilter

67

Application Example: Bing News
events happening in Egypt
protest in Egypt

Obama government’s actions

budget/spending
republican/campaign

68Application Example: Bing News(a)(b)69

Application Example: Bing News70

Video71

Outline Example tasks in text analytics
 Visually analyzing textual information– Dynamic word clouds– Topic-based visual text summarization– TextFlow: towards better understanding of evolving
topics in text

 Future work

72

Future Text Visualization Topics
 Interactive, incremental text analytics
 Multi-level visual text summarization (keywords+ sentences) Multi-faceted text analytics (e.g., summarization +sentimental analysis) Multimedia document summarization (text +image + video) Interactive, visual social media analysis

73

Acknowledgements
Weiwei Cui, Yangqiu Song, Furu Wei, Xin Tong (MSRA)
Nan Cao, Yingcai Wu, Prof. Huamin Qu (HKUST)
Dr. Michelle X Zhou (IBM Almaden Research Center)74