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Visualization and Analysis of Text

Remco Chang, PhDAssistant Professor

Department of Computer ScienceTufts University

December 17, 2010Cologne, Germany

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CMVVis Examples P Topics

Introduction

• Information Visualization– Novel visual representations– Storytelling– User-Driven

• Visual Analysis– Data exploration– Hypotheses generation– Interactive visualization + Computation

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Visualization

• Pre-attentive Processing

Examples courtesy of Chris Healey

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Visualization• This is helpful

because:

– It allows us to process more information quickly

– We can see trends and patterns

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Storytelling

• US Budget from 1961 - 2008

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Storytelling

• Minard’s Map:

• Napolean’s March to Moscow

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Visualization

• Influences the thought…

Images courtesy of Barbara Tversky

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Visual Encoding

• Affects the:

– Types of possible operations

– The user’s thinking process

Zhang and Norman. The Representation Of Numbers. Cognition. (1995)

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Classifying Numeric Systems

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Example: Arithmetic

Slide courtesy of Pat Hanrahan

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Example: Arithmetic

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Example: Arithmetic

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Example: Arithmetic

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Examples of Text Visualization

• Wordle

Images Courtesy of Many Eyes

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• WordTree

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• WordTree

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• Phrase Net

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• Google Auto-Complete

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• Visualizing changes in Wikipedia

Images Courtesy of Info.fm

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• ThemeRiver

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Visual Exploration

• Coordinated Multi-Views (CMV)

Where

When

Who

What

Original Data

EvidenceBox

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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

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LIDAR Linked Feature Space

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LIDAR Change Detection

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Urban Model

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Urban Visualization

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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

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inspection reports



comparison methods

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inspection reports



comparison methods

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CMV + Text Analysis

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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

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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

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Topic Modeling

• Given a set of k documents, find n number of topics– Each document then is described as:• (W1 * Topic1, W2 * Topic2, W3 * Topic3, …, Wn * Topicn)

• W1 + W2 + W3 + … + Wn = 1

Topic 1 Topic 2 … Topic NDocument 1 0.12 0.68 … 0.005Document 2 0.3 0.06 … 0.01…Document K

∑ = 1

∑ = 1...

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Topic Modeling

• A topic is a combination of keywords

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Parallel Topics

• Based on “Parallel Coordinates”– Each vertical axis is a topic– Each set of horizontal connected lines is a

document

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Visual Signatures

Single topic Bi-topic

No salient topic

• We identify different signatures for proposals:– Single Topic – focused research– Bi-Topic – Interdisciplinary research– No-Topic – Potentially transformative research

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Selecting Single Topic Proposals

Topic 1 Topic 2 … Topic N

Document 1 0.12 0.68 … 0.005

Document 2 0.3 0.06 … 0.01

…

Document K

SD = 0.14SD = 0.06

Max

SD

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Selecting Multi-Topic ProposalseducationtechnologyInteractive

environment

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Selecting No-Topic Proposals

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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

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Questions and Comments?

Thank you!!

[email protected]://www.cs.tufts.edu/~remco