# Presentation: 729036

## Marti Hearst
School of Information, UC Berkeley

**School of Information, UC Berkeley**

**Visualization in Text Analysis Problems**

**VAC Consortium Meeting****Stanford, May 24, 2006**

## Outline

- Some Visualization Design Principles
- Illustrated with a new example
- Why Text is Tricky to Visualize
- How to do good visualization design with text while meeting analysts needs?
- Focus on* Flexibility with Reproducibility*
- Examples from 4 different domains

## What Makes for a Good Visualization?

- Visually illuminates important aspects of the underlying data and domain.
- Supports the users’ tasks (better than without the visualization).
- Adheres to good design principles.

## Example from Software EngineeringMarat Boshernitsan, UC Berkeley PhD Dissertation 2006

- Problem: need to make complex changes throughout code.
-  Example: convert from one API to another.

## A Typical Solution

- Either requires programmers to understand and manipulate abstract syntax trees ...
- Or requires learning another programming language (or both)!

## First Attempt

## Second Attempt

## A Better Solution

- Build on how programmers think about programming.
-  Operate on the textual representation of code.

## Users Operate on Familiar Visual Representation of Code

## Context-and-Domain Sensitive Visual Cues

## Lessons from this Example

- User-centered Design
- This was the third attempt.
- First 2 attempts did not accurately reflect how users think about the problem.
- Careful design of labels and interaction cues
- Very intelligent backend, but user-activated.
- Visually and interactively reflects how programmers think about programming.

## What Makes for a Good Visualization   for Analysts?

- Visually illuminates important aspects of the underlying data and domain.
- Supports the users’ tasks (better than without the visualization).
- Adheres to good design principles.

## Goals vs. Tasks

- Analysts’ Goals:
- Understand current and past situations
- Predict and anticipate future situations
- Observations by Pirolli & Card ’05:
-  Different analysts starting with people, organizations, tasks, and time:
-  predict coup likelihood
-  understand bio-warfare threats
-  understand relations within cartel

## Goals vs. Tasks

- Analysts’ tasks:
- Explore
- Extract
- Filter
- Link
- Arrange
- Compare
- Hypothesize
- (A combination of Foraging and Sensemaking)
- Should do the tasks *only* to support the goals.

## Design Principles for Analysts

- Experienced analysts notice what is *missing* or *unexpected* (Wright et al. ’06)
- Thus consistency and reproducibility are important.

## Design Principles for Analysts

- Analysts must guard against confirmation bias. (Pirolli & Card ’05)
- Thus it is important for analysts to
- Be able to easily arrange and re-arrange,
- View information flexibly from many angles,
- 	While at the same time retaining consistency and reproducibility.
- However ... it’s hard to do this with text.

##  Working with Text Text is especially difficult to visualize

- Very high dimensionality
- Tens to hundreds of thousands of features
- Compositional
- Can be combined together in innumerable ways
- Abstract
- And so difficult to visualize
**Not** pre-attentive
- Must foveate to read
- Subtle
- Small differences matter
- Unordered

## Text Meaning is NOT pre-attentive

- SUBJECT PUNCHED QUICKLY OXIDIZED  TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO
- CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM
- SCIENCE ENGLISH  RECORDS COLUMNS  ECNEICS HSILGNE  SDROCER  SNMULOC
- GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM
- CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM
- GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM
- SCIENCE ENGLISH  RECORDS COLUMNS  ECNEICS HSILGNE  SDROCER  SNMULOC
- SUBJECT PUNCHED QUICKLY OXIDIZED  TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO
- CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM
- SCIENCE ENGLISH  RECORDS COLUMNS  ECNEICS HSILGNE  SDROCER  SNMULOC

## Why Text is Tough

- Abstract concepts are difficult to visualize
- Combinations of abstract concepts are even more difficult to visualize
- time
- shades of meaning
- social and psychological concepts
- causal relationships

## Why Text is Tough

**Why Text is Tough**

**The dog.**.

**Why Text is Tough**

## Why Text is Tough

**Why Text is Tough**

**The dog.**

**The dog cavorts.**

**The dog cavorted.**

**Why Text is Tough**

## Why Text is Tough

**Why Text is Tough**

**The man.**

**The man walks.**

**Why Text is Tough**

## Why Text is Tough

**Why Text is Tough**

**The man walks the cavorting dog.**

- So far, we can sort of show this in pictures.

**Why Text is Tough**

## Why Text is Tough

**Why Text is Tough**

**As the man walks the cavorting dog, thoughts**
**arrive unbidden of the previous spring, so unlike**
**this one, in which walking was marching and**
**dogs were baleful sentinels outside unjust halls.**

- How do we visualize this?

**Why Text is Tough**

## Why Text is Tough

- Language only hints at meaning
- Most meaning of text lies within our minds and common understanding
- “How much is that doggy in the window?”
-  how much: social system of barter and trade (not the size of the dog)
-  “doggy” implies childlike, plaintive, probably cannot do the purchasing on their own
-  “in the window” implies behind a store window, not really inside a window, requires notion of window shopping

**Why Text is Tough**

## Why Text is Tough

- General categories have no standard ordering (nominal data)
- Categorization of documents by single topics misses important distinctions
- Consider an article about
- NAFTA
- The effects of NAFTA on truck manufacture
- The effects of NAFTA on productivity of truck manufacture in the neighboring cities of El Paso and Juarez

**Why Text is Tough**

## Why Text is Tough

- Other issues about language
- Ambiguous (many different meanings for the same words and phrases)
- Same meaning implied by different combinations
- Different combinations imply different meanings

## Why Text is (Deceptively) Easy

- Text is easier when you have a lot of it
- Web search is now usually conjunction
- Text has a lot of redundancy
- A very simple algorithm can:
-  Pull out “important” phrases
-  Find “meaningfully” related words
-  Create a “summary” from document
-  Group “related” documents

## Why Text is Easy

- Pretty much any simple technique can pull out phrases that seem to characterize a document
- Most frequent words from an IR lecture :
- 109    slide        69   to           37     view         37    version    37       graphic  37   first
- 37      back        36   previous  36     next         32    of           31       the
- 30      recall       28   relevant  27      precision  25    retrieved  25       documents
- 21      and         18   evaluate 15       a             13    what       13       vs         13   how
- 12      trec         12   is           12       high        12    for          10       relevance
- 10      queries    10   on            9      information  8   x             8      why
- 8        as            8    answer     7      search        7    maron      7       document
- 7        blair         6    top           6       results      6     measure
- 6        length      6    in             6       evaluation  6     curves

## Why Text is Easy

- Same text, removing most frequent words in language and most frequent in this text:
- 30      recall       28   relevant     27      precision   25    retrieved  25    documents
- 18      evaluate   13   vs             12      trec          12    high        10    relevance
- 10      queries      9  information   8      x               8     answer     7    search
- 7        maron       7  document     7     blair           6     top           6    results
- 6        measure    6    length        6     evaluation   6     curves
- These words can act as a simple summary of the document
- People are good at inferring (sometimes inventing) the commonalities
- People are bad at realizing what they are *not* seeing

## Simple Text Analysis can Mislead

- Most frequent words
- Biases towards concepts with unique identifiers.

- From Spink, Wolfram, Jansen, Saracevic, JASIS ‘01

## Major Trends vs. Minor Discoveries

- With text, it’s easy to extract and show the largest, main trends
- But often we want the rare but unexpected and important event:
- Russian oil company example
- Schwarzenegger and Enron
- Cigarettes and kids
- Person on the periphery who is working stealthily to influence things
- This is really difficult to solve!

## Design Principles for Analysts

- Experienced analysts notice what is *missing* or *unexpected*.
- Analysts must guard against confirmation bias.
- Need to be able to easily arrange and re-arrange,
- View information flexibly from many angles,
- While at the same time retaining consistency and reproducibility.
- Interfaces should reflect the domain and data.
- How to achieve this with text collections?
- Must transform text in understandable ways
- Must provide multiple, consistent views that nevertheless allow for new discovery and insight

## Why Emphasize Flexibility?

- Can’t view representations of all the text content at once.
- Instead, needs ways to flexibly navigate, group, organize, explore
- See important pieces over time.

## The Importance of Flexibility

- Russell, Slaney, Qu, Houston ’05
- The ease of viewing and manipulation in the system strongly influenced the kind of analysis operations done.

## Examples of Flexibility on Text Data

- PaperLens (Conference proceedings)
- TAMKI (Customer service requests)
- Faceted Browsing  (e-commerce)
- Flamenco
- Ebay Express
- FaThumb
- TRIST and Sandbox (Analysts)

## Flexible views

- Infoviz 2004 contest
- Visualize 8 years of conference proceedings
- Tasks:
- Static Overview of 10 years of Infovis
- Characterize the research areas and their evolution
- The people in InfoVis
- Which papers/authors are most often referenced?
- How many papers conducted a user study?
- PaperLens integrated solution by Lee, Czerwinski, Robertson, Bederson
- Uses graphical elements and brushing and linking to flexibly elicudate a collection’s contents.
- _[http://www.cs.umd.edu/hcil/InfovisRepository/contest-2004/index.shtml](http://www.cs.umd.edu/hcil/InfovisRepository/contest-2004/index.shtml)_

## Flexibility in Foraging and Analysis

- TAKMI, by Nasukawa and Nagano, ‘01
- The system integrates:
- Analysis tasks (customer service help)
- Content analysis
- Information Visualization

## Flexibility in AnalysisTAKMI, by Nasukawa and Nagano, 2001

**Flexibility in Analysis****TAKMI, by Nasukawa and Nagano, 2001**

- Documents containing “windows 98”

## TAKMI, by Nasukawa and Nagano, 2001

**Flexibility in Analysis****TAKMI, by Nasukawa and Nagano, 2001**

- Patent documents containing “inkjet”, organized by entity and year

## Flexibility in Category Navigation

- Browsing Information Collections using (Hierarchical) Faceted Metadata

## What are facets?

- Sets of categories, each of which describe a different aspect of the objects in the collection.
- Each of these can be hierarchical.
- (Not necessarily mutually exclusive nor exhaustive, but often that is a goal.)

## Facet example: Recipes

## Nobel Prize Winners Collection

## Slide 47

## Slide 48

## Slide 49

## Slide 50

## Slide 51

## Slide 52

## Slide 53

## New Site: eBay Express

## Slide 55

## Slide 56

## Slide 57

## Slide 58

## Slide 59

## Slide 60

## Slide 61

## Is This Visualization?

- Prior experience and other people’s attempts seem to suggest that fewer graphics and more text is better.
- Details of layout, font and color contrast, label selection, and interaction make all the difference.

## Earlier Variation on the Idea

- Cat-a-Cone, 1997

## Mobile Variation

- FaThumb: Karlson, Robertson, Robbins, Czerwinski, Smith ’06
- Well-received, but visualization part not looked at.

## Flexibility in SenseMaking

- DLITE by Cousins et al. ‘97
- Sandbox by Wright et al. ‘06

## Flexibility in Sensemaking

- Entities

- Dimensions

**TRIST** (**T**he **R**apid **I**nformation **S**canning **T**ool) is the work space for Information Retrieval and **Information Triage**.

- Launch Queries

- AnnotatedDocument Browser

- Comparative Analysisof Answers and Content

- User Defined andAutomatic Categorization

- Rapid Scanningwith Context

- Linked Multi-Dimensional Views Speed Scanning

**Flexibility in Sensemaking**

- TRIST, Jonkers et al 05

## Flexibility for Sensemaking Support

**Flexibility for Sensemaking Support**

- Quick Emphasis of Items of Importance.

**Sandbox, Wright et al ‘06**

- Direct interactionwith Gestures(no dialog, no controls).

- DynamicAnalytical Models.

- Assertions with Proving/Disproving Gates.

## Communication-Centric Text

- Email, conversations, blogs
- The first thought is usually nodes and links
- Doesn’t have the desired flexibility
- Some alternatives:
- The Network
- Multivariate Networks

## Re-envisioning Networks

- Viewing people’s shared workplaces, hometowns, schools over time.
- www.theyrule.net:

## Re-envisioning Networks

- First cut:
- Hastings, Snow, and King ’05

## Re-envisioning Networks

- Better version:
- Hastings, Snow, and King ’05

## Re-envisioning Networks

- Wattenberg ’06
- OLAP on directed labeled graphs

## Network Flexibility

**Network Flexibility**

**Notes:**
Gender plus another binary attribute

## Martin Wattenberg, “Visual Exploration of Multivariate Graphs”

- M

- F

- Location A

- Location B

- Location C

- Location D

- Location E

**Notes:**
Node size indicates number of people at intersection of Location and Gender attributes.  Arcs indicate communication from people in one class to people in another.

## Re-envisioning Networks

- Idea: vary these ideas to apply to email and other communication text.

## Summary:Text Viz Design Guidelines

- An emphasis on flexible views on text data
- Emphasize brushing and linking using appropriate visual cues.
- Interaction flow should guide the user but also be flexible.
- Information structure should be consistent and reproducible.
- Other guidelines:
- Make text visible.
- Visual components should reflect the data and tasks.

## Thank you!

- www.sims.berkeley.edu/~hearst