Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and...

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A text-based model of foreign-affairs sentiment Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011

Transcript of Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and...

Page 1: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

A text-based model of foreign-affairs sentiment

Sean Gerrish and David BleiPrinceton University Computer Science

17 December 2011

Page 2: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

These news articles tell a story.

Page 3: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles
Page 4: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles
Page 5: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

A spatial model of foreign relations sentiment

This work develops a model of the sentimentbetween countries over time.

• It models dynamic relationships in aninterpretable way

• It infers sentiment from printed media

• Sentiment is defined by Mechanical Turkers

Page 6: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

A spatial model of foreign relations sentiment

To do this, our plan is to:

• Collect a bunch of newspaper articles

• Define a latent variable model to captureinteresting structure in these articles

• Perform posterior inference to estimate thevalue of these random variables

Page 7: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Countries take latent positions x̄ct over time

Time

x

0

-2-1

1 2

x̄c ,t|x̄c ,t−1 ∼ N(x̄c ,t−1, σ2K)

Page 8: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

The relationship between countries is observed in the news.

Time

x

0

-2-1

1 2

xc1,d ∼ N(x̄c1,t, σ2D)

xc2,d ∼ N(x̄c2,t, σ2D)

Sentiment sd := xc1,dTxc2,d

Page 9: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

The relationship between countries is observed in the news.

Time

x

0

-2-1

1 2

xc1,d ∼ N(x̄c1,t, σ2D)

xc2,d ∼ N(x̄c2,t, σ2D)

Sentiment sd := xc1,dTxc2,d

Page 10: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

The relationship between countries over time

xc,t-1 xc,t xc,t+1

xc,t-1 xc,t xc,t+1

C

Dt-1 t t+1

w

sss

ww

zerozero zero

2 2 2

DD

β

Latent position

Position duringinteraction

Sentiment

Observed text

Regularization

Page 11: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Labeling sentiment

1. We found all pairs of paragraphs from the New York Timeswhich discussed exactly two countries

2. A random sample of 3607 paragraphs from New York Timesarticles from 1988 to 2008 were labeled by AmazonMechanical Turk workers

3. Raters rated news articles on the scale −5,−3,−1, 1, 3, 5

Page 12: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Labeling sentiment: typical task

Page 13: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Sentiment and news articles: text regression

sd = wTd β + ε

• wd ∈ RV is the text of a news paragraph

• sd ∈ R is the sentiment between two countries

• β ∈ RV is the “weight” of each word

Page 14: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Sentiment and news articles: text parameter β

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covered

crash

flew

satellites

flee

fled

successor

articles

transitional

artists

related

doubled

disclose

averted

promptly

conflicts

proposing

reversed

institution

isolation

linked

afraid

agency

appealing

combat

alarmedneighboring

gain

electoral

agreed

fearoperational

local

ones

married

barrels

hardship

closer

favor

identification

closed

ability

agencies

job

grain

tactical

unclear

wall

withdrawn

improved

cross

unity

largest

diplomat

student

correspondent

fighting

rocket

heavily

obtain

republics

press

taxpayers

agents

employment

ravaged

lead

leader

murdered

extension

getting

spilled

sectarian

shipping

surge

carries

promisetransfer

intention

funds

challenges

record

demonstrate

firm

percent

book

conclusion

illegally

−0.5 0.0 0.5

Page 15: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Experiments

• Randomly select 3607 paragraphs discussing pairs of 245countries and territories.

• Label each of these paragraphs’ sentiment with two ratingsfrom Amazon Mechanical Turk.

• Hold out 42 random pairs (244 paragraphs) for testing.

• Fit sentiment model parameters β on training paragraphs.

• Infer the spatial sentiment model with these parameters on all257,472 paragraphs from 1988 to 2008.

Page 16: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Analysis with this model

To perform analysis with this model:

1. Fit the posterior (we used MAP)

2. Inspect countries’ means x̄c,·

3. Inspect the relationship between countries’ means x̄c1,t x̄c2,t

Page 17: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Results: selected countries’ latent positions

Ip1

Ip2

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

afghanistan

canada

china

france

indiairaq

israel

japannorth_koreapakistanrussia/ussr

south_korea

switzerland

united_states

−0.2 0.0 0.2 0.4 0.6 0.8 1.0Ip1

Ip2

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

afghanistancanada

china

franceindia

iraq

israeljapan

north_korea

pakistan

russia/ussr

south_koreaswitzerland

united_states

0.0 0.5 1.0

1987 2007

Page 18: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Results: selected countries’ mutual sentiment with the U.S.

Date

Mut

ual s

entim

ent w

ith th

e U

nite

d S

tate

s

−0.6

−0.4

−0.2

0.0

0.2

0.4

−0.6

−0.4

−0.2

0.0

0.2

0.4

−0.6

−0.4

−0.2

0.0

0.2

0.4

afghanistan

iraq

russia/ussr

1990 1995 2000 2005

canada

israel

switzerland

1990 1995 2000 2005

china

japan

yugoslavia

1990 1995 2000 2005

Page 19: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Results: selected countries’ mutual sentiment with Spain

Date

Mut

ual s

entim

ent w

ith S

pain

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

afghanistan

great_britain

portugal

1990199520002005

china

greece

united_states

1990199520002005

france

iraq

1990199520002005

germany

italy

1990199520002005

Page 20: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Evaluation

The model does better than text regression and individualMechanical Turk workers compared against one another.

Model Mean Squared Error Mean Absolute Error

Inter-rater agreement 1.77 (7.11) 1.037 (2.07)

Text regression 5.53 1.94

Reversion variance 0.1 2.36 1.09Reversion variance 1 2.32 1.07

Reversion variance 10 2.32 1.08Reversion variance 100 2.34 1.09

Reversion variance 1000 2.33 1.08

Page 21: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Current work and future directions

• Sentiment intercepts for each country

• Infer asymmetric relationships

• Application to other dyads

• Infer unsupervised relations• Sentiment is only one dimension• Similar to relational topic models [1]

Page 22: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Thank you

• Sean Gerrish ([email protected])

• David Blei ([email protected])

Page 23: Sean Gerrish and David Blei Princeton University …wallach/workshops/nips2011...Sean Gerrish and David Blei Princeton University Computer Science 17 December 2011 These news articles

Bibliography

Chang, J., D. M. Blei.

“Relational topic models for document networks.”

Proceedings of the 12th International Conference on ArtificialIntelligence and Statistics (AIStats) 2009, 5, 2009.