The Linguistics of Spin Stephan Greene Philip Resnik MITH: November 13, 2007.

The Linguistics of Spin

Stephan GreenePhilip Resnik

MITH: November 13, 2007

2MITH: November 13, 2007

We All Know What Spin is…

“Mistakes were made.”

- Ronald Reagan, January 27, 1987


Spin is…

A distinctive point of view, emphasis, or interpretation

• The American Heritage® Dictionary of the English Language, Fourth Edition

A special point of view, emphasis, or interpretation presented for the purpose of influencing opinion

• Merriam-Webster's Online Dictionary, 10th Edition


Example: Does Reuters Spin in Favor of the Palestinians?

Israelis did X. Palestinians did X.

Claims made by pro-Israeli media watchdog group honestreporting.com


Example: Does Reuters Spin in Favor of the Palestinians?

Israelis did X. Palestinians did X.

What should be the basis for an objective analysis?


An Aside: Opinion is not Spin

Product Reviews

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Movie Reviews

“Have you gotten the point that I LIKE THIS MOVIE?”


A Hypothesis, Introduced via an Analogy

Facial expressions

Emotion

Construal

Sentiment

Linguistic expression

Anger

Terrorists destroyed the bus

The bus exploded

CausationIntent

Change of State…

Negative

The bus was dispatched

Ekman (2002)


Surface expression and the construal of events

• Language supports multiple perspectives on the same event

• The cat chased the mouse

• The mouse fled from the cat

• Syntactic choices and semantic construals are connected

• X is similar to Y means

Y is similar to X, right?

• Vietnam is similar to Iraq

• Iraq is similar to Vietnam

Positioning of the noun phrases determines figure/ground or variant/referent construal

(Gleitman et al. 1996; cf. Tversky 1977)


Surface expression and the construal of events

• A limited conceptual vocabulary accounts for how

semantic construals are encoded by syntactic forms.

• The farmer loaded hay onto the wagon

• The farmer loaded the wagon with hay

• The farmer tossed hay onto the wagon

• *The farmer tossed the wagon with hay Change of state

(Pinker, Jackendoff, Levin, Dowty, and many others)


Elements of Linguistic Expression

Construal

Sentiment



The bus exploded

CausationIntent

Change of State…

Negative


Word choice

Syntactic frame

kill

butcher

murder

martyr


Example: Entailments of Verb Choices

0

1

2

3

4

5

6

7

Volition Sentience Punctuality Telicity

Ra

tin

g Ergative Kill Verbs

Transitive Kill Verbs

(a) The witch poisoned Snow White

(b) The witch drowned Snow White

Did the witch intend to do what she did?

People attribute more intent to the witch when the action was to poison Snow White.

The difference, though small, is statistically significant.

(Greene 2007)

*Snow White poisoned.

Snow White drowned.

(Levin 1993, Lemmens 1998)


Overview

Introduction

Connecting Lexical Semantics with Perceived Sentiment: Psycholinguistic Evidence

Automatically Labeling Documents with Sentiment

• The Death Penalty Debate

• The Israeli-Palestinian Conflict

• Congressional Floor Debate Speeches

Future Directions


Lexical Semantics → Perceived Sentiment

Intuitions on encoding choices: Who did what to whom?

• volitional agents

• causative verbs

• affected objects, …

Connection: Independently motivated, formally investigated work in linguistics

• Decompositional semantics for Transitive clauses

• Differences in linguistic form correlate with differences in meaning

First Result: Established Semantic Components predict Perceived Sentiment


What Semantic Properties Matter?

Sources

Dowty (1991) Proto-Role PropertyHopper and Thompson (1980) Transitivity Component

Proto-Agent Properties

• Volitional involvement in the event or state • Volition

• Sentience (and/or Perception)

• Cause event or change of state in object • Agency

• Movement relative to the position of another participant

• Kinesis

• Exists independently of the event

Proto-Patient Properties

• Undergoes change of state, Causally effected by another participant

• Affectedness of Object

• Does not exist independently of the event

• Stationary relative to movement of another participant

• Kinesis

• Object individuation

• (Exists independently of the event?) • Subject-object individuation

• (Incremental Theme?) • Punctuality


A Human Subject Experiment

Construal Sentiment


The men destroyed the bus

CausationIntent

Change of State…


The men destroyed the bus

How sympathetic to

the men?

How well does the semantic construal of the event predict sympathy toward

the one(s) responsible for the

event?


Rating Semantic Components of Transitivity for Different Linguistic Encodings of the Event

Telicity

Punctuality

Subject-Object Individuation

Kinesis

Affectedness of Object

Volition

Agency

Examples:

“Man suffocates 24 year-old woman.” “Suffocation kills 24-year old woman.”


Rating Event Encoding Sentiment

Suffocation kills 24-year old woman.


Analysis:Semantic Components Predict Perceived Sentiment

Suffocation kills 24-year old woman.


Analysis:Semantic Components Predict Perceived Sentiment

Man suffocates 24-year old woman.

The semantic variables account for 81.9% of the variance.

100% would be perfect prediction.


Additional Results: Event Encoding Distinctions

Human Agent

• “The men killed eight marketgoers.”

Nominalized Agent

• “The explosion killed eight marketgoers.”

Sentiment rating difference is statistically significant at p < .001

0

0.5

1

1.5

2

2.5

3

3.5

4

Sym

pat

hy

(Sen

tim

ent)

Rat

ing

Human Agent

Nominalized Agent

If you express the same event using a “nominalized agent” – the explosion killed rather than the terrorists killed – people interpret the description as significantly more sympathetic to the men.


Additional Results: Our Statistical Analysis Supports Linguists’ Analysis

Principal Components Analysis corresponds with Agent and Patient proto-role properties

Component

1 2 3

Cause Event .930 -.031 .146

Sentience .925 -.307 .133

Volition .920 -.326 .127

Subject - Object Individuation .916 .091 -.011

Independent Existence .903 -.222 .107

Kinesis (Movement) .871 -.328 .274

Kinesis (Stationary) .639 .360 -.471

No Independent Existence .350 -.274 -.264

Causal Effect .438 .790 -.088

Object Individuation .453 .695 .007

Cause Change of State .579 .618 -.128

Verb .131 .244 .103

Punctuality .056 .128 .901

Telicity -.390 .450 .673


What Have We Shown?

Construal

Sentiment



The bus exploded

CausationIntent

Change of State…

Negative


Word choice

Syntactic frame

kill

butcher

murder

martyr


Underlying Semantics, Event Encodings, and Sentiment: Applying the formalized connections to a practical task

Computational Text Classification with respect to Implicit Sentiment

Example Applications: Information services for intelligence analysts, policy analysts, …


Text Classification: Supervised Machine Learning

Example: Spam Filtering

Input: training data (x1,y1), (x2,y2),…, (xn,yn)

• Each xi is a set of observable features representing an item

• Each yi is a (binary) label (e.g. spam, not spam)

Supervised Machine Learning produces a Classifier

• Input to the Classifier is unlabeled items: (x,?)

• Output is a prediction of the label y: (x,?) → (x,y)

Primary Considerations

• Learning algorithm

• Item representation (feature choice)


Feature Definition: Translating Semantic Components into Practical Text Representation

Domain Relevant Terms

• Kill verbs and nominalizations

• Automatically identified terms

Grammatical Relations

• Dependency Parse

Terms + Relations = Observable Proxies for Underlying Semantics (OPUS)

murdered

they


Feature Production

Sentence:

Life Without Parole does not eliminate the risk that the prisoner will murder a guard, a visitor, or another inmate.

Constituent Parse:

(S

(NP (DT the) (NN prisoner))

(VP (MD will)

(VP (VB murder)

(NP

(NP (DT a) (NN guard))

(, ,)

Grammatical relations:

nsubj(murder, prisoner)aux(murder, will)dobj(murder, guard)

OPUS features:

TRANS-murdermurder-nsubjnsubj-prisonermurder-auxaux-willmurder-dobjdobj-guard


Kill Verbs: Constructing The Death Penalty Corpus

Web Site Number of Documents

PRO: www.prodeathpenalty.com (PRO1) 117 www.clarkprosecutor.org (PRO2) 437 www.yesdeathpenalty.com (PRO3) 26 www.thenewamerican.com (PRO4) 7 www.dpinfo.com (PRO5) 9 596 Total CON: www.deathpenaltyinfo.org (CON1) 319 www.nodeathpenalty.org (CON2) 212 www.deathpenalty.org (CON3) 65 596 Total


Implicit Sentiment Classification: Value of OPUS Features

Task: Document classification, PRO/CON Death Penalty

Evaluation: two-by-two site-wise cross validation

Feature Set Naïve Bayes C4.5 SVM

baseline n-grams 68.63 71.63 68.37

OPUS (kill verbs) 82.42 77.84 82.09

n+ = 295

n- = 84

p < 0.0001

n+ = 303

n- = 208

p < 0.0001

n+ = 362

n- = 152

p < 0.0001


Beyond the Kill Verbs: Can the Method be Generalized?

Kill Verbs were chosen because:

• They have been studied extensively

• Lexical semantics suggest they are prototypically transitive

…the Death Penalty Corpus was built around them

Real problems usually work the other way:

• The documents exist

• How do we choose terms to target?

• Solution: Corpus-driven automatic identification of terms


Generalization: Domain Relevant Terms

Relative Frequency Ratio:

Examples:

‘kill’

‘rob’

‘force’

08.101000000

157

642279010160

rfR

rc

rc

dc

dc

NF

NF

rfR

97.221000000

100

64227901475

rfR

51.11000000

113

64227901101

rfR

dc = domain corpus

rc = reference corpus (BNC)


Automatically Identified Verbs from the DP Corpus

testify, convict, sentence, execute, aggravate, file, strangle, affirm, stab, schedule, rape, rob, violate, overturn, accord, murder, confess, pronounce, plead, shoot, kill, deny, arrest, condemn, commit, fire, witness, request, steal, review, appeal, decline, grant, rule, die, reject, state, impose, conclude, question, charge, beat, drive, attempt, release, admit, refuse, present, recommend, conduct, order, serve, receive, argue, determine, suffer, seek, issue, claim, note, discover, enter, fail, strike, find, identify, result, return, tell, include, indicate, arrive, sign, force, stop, say, pull, support, reveal, live, raise, ask, visit, drop, believe, hear, love, represent, regard, occur, hit, decide, express, involve, prove, stay, walk, consider, write, spend, end, place, fight, plan, face, base, continue, leave, call, hold, watch, allow, try, obtain, cause, begin, set


Implicit Sentiment Classification:Automatically Chosen Verbs

Feature Set SVM

baseline bigrams 71.96

OPUS features (“linguistically relevant” verbs)

82.09

OPUS features(automatically chosen verbs)

88.10

n+ = 367, n- = 149, p < 0.001

n+ = 206, n- = 151, p < 0.01


The Israeli-Palestinian Conflict

The Bitter Lemons Website

The Bitter Lemons Corpus (Lin et al. 2006)

Palestinian Israeli

Editors 148 149

Guests 149 148

Total number of documents 297 297

Average document length 740.4 816.1

Number of sentences 8963 9640


November 12, 2007 Edition 41 Is the PA beginning to resemble the SLA?

two Palestinian views1.The PA cannot remain transitional

much longer

by Ghassan Khatib

The situation in Nablus can only further discredit the PA in the eyes of its own public and strengthen comparisons with the SLA.

two Israeli views1.The danger is there by Yossi Alpher

Any comparison between Abbas/Salam and Lahd is, to say the least, not flattering.

1.Unite or dissolve an interview with Eyad Sarraj The Palestinians have lost the ability to govern themselves, to make war or to make peace.

1.The political context is totally different

by Dani Reshef The SLA sought to join the Lebanese army as a territorial brigade.


Implicit Sentiment Classification: BL Corpus Results

70 Experiments

• Avg accuracy: 95.67%

• Max accuracy: 97.64%

Previous Work (Lin 2006)

• NB-B 93.46%

• SVM 88.22%

Classification Accuracy, BL CorpusTest Scenario 1 (DeterminerFilter)

0

2

4

6

8

10

12

Individual Experiment (ρ values and accuracy)

Ter

m T

hre

sho

ld (

ρ)

84

86

88

90

92

94

96

98

Per

cen

t C

orr

ect ρ (Verb)

ρ (Noun)

OPUS

Lin 2006 NB-B

Lin 2006 SVM


Congressional Floor Debate Speeches: The CS Corpus (Thomas, Pang, and Lee 2006)

Classification Task: binary YEA/NAY (for or against) the legislation under debate

corpus total

training set

test set

development set

speech segments 3857 2740 860 257

debates 53 38 10 5

average number of speech segments

72.8 72.1 86.0 51.4

average number of speakers per debate

32.1 30.9 41.1 22.6


Implicit Sentiment Classification: Results for CS Corpus

Maximum accuracy: 70.00% (p < 0.05)

Previous Work: 66.05% (Thomas, Pang, and Lee 2006)

SVM Classifier Accuracy

64

65

66

67

68

69

70

71

Term Threshold (ρ )

Per

cen

t C

orr

ect

OPUS-SVM 68.84 69.53 70.00 69.19 68.26 68.84 69.42 68.26 67.79

U-SVM (TPL06) 66.05 66.05 66.05 66.05 66.05 66.05 66.05 66.05 66.05

0.0 0.2 0.4 0.6 1.0 1.5 2.0 2.5 3.0


Adding Inter-Item Relationships(following Thomas, Pang, & Lee 2006)

Congressional Speech exhibits:

• Repeated speakers

• References to other speakers

These relations can be exploited:

• Same-speaker links: Assume a speaker always exhibits the same orientation within a debate

• Agreement links

— By-name references to other speakers are automatically classified as agreement or disagreement

— Instances of agreement are ‘soft’ versions of same-speaker links


Congressional Speech: Example

It is not even clear we can move it to another bill at this point. Yet, it is the only bill standing, and it is a bipartisan effort to try to address this scourge that is crossing the country. I thank Chairman Sensenbrenner; also majority leader Roy Blunt, who has been an early leader in this charge; Chairman Barton of the energy and commerce committee for his willingness to have this. I would also thank the several members who have worked so hard to make this comprehensive anti-meth legislation happen. In particular, I would like to thank Representatives Mark Kennedy, Darlene Hooley of Oregon, Dave Reichert and John Peterson, because they provided much of the content of this comprehensive bill and their consistently strong leadership on the house floor.

I would also like to thank the four co-chairmen of the congressional meth caucus, Congressmen Larsen, Calvert, Boswell and Cannon, for their staffs' assistance in putting this together so we could have a bipartisan effort. Congressman Tom Osborne has crusaded on this house floor and across the country on behalf of anti-meth legislation, as has Congressmen Baird, Wamp, Boozman, King, Gordon and so many others. This would not be happening today if we did not have this bipartisan coalition, and I hope it becomes law.


SVM Classification: a Graph Minimum Cut Representation


Classification Framework with Inter-Item Relationships


Classification with Inter-Item Relationships: Results

YEA or NAY classification of speech segments

U-SVM (Thomas, Pang and Lee 2006)

OPUS-SVM

SVM only 66.05 70.00 (p < 0.05)

SVM arcs plus same-speaker arcs 67.21 70.81 (p < 0.05)

SVM arcs plus same-speaker arcs and agreement arcs, θagr = 0

70.81 68.37

SVM arcs plus same-speaker arcs and agreement arcs, θagr = μ

70.81 70.93

SVM arcs plus same-speaker arcs and agreement arcs, θagr = 1.5μ

67.33 70.12



Summary

Facial expressions

Emotion

Construal

Sentiment


Anger


The bus exploded

CausationIntent

Change of State…

Negative


Ekman (2002)

The way language encodes underlying sentiment is mediated by a “conceptual lens”, afforded by systematic dimensions of semantic construal.

This fact can be exploited for practical tasks.


Where Do We Go From Here?

JUSTICE KENNEDY: Do you know, since you seem to have looked at it: In North Carolina, for the person whose had his civil rights taken away, is -- is there any mechanism to get them back earlier by -- by applying for clemency or a pardon or -

JUSTICE GINSBURG: So if you had a statute -- a State like, I'm told, Vermont, that doesn't take away any one's civil rights, not even a first degree murderer's, then that first degree murderer would be equated to someone whose civil rights were taken away and then restored.


Where Do We Go From Here?

Brent Cunningham, The Rhetoric Beat: Why journalism needs one

Columbia Journalism Review — November / December 2007

What if on 9/11 our major media outlets had employed reporters whose sole job it was to cover the rhetoric of politics— to parse the language of our elected leaders, challenge it, and explain the thinking behind it, the potential power it can have to legitimize certain actions and policies and render others illegitimate? …

Apologies to William Safire, but journalism needs a rhetoric beat. Yes, language has been used and misused in the service of politics since man first had both language and politics. Political rhetoric is not inherently bad, and I am not suggesting a War on Rhetoric. But there are aspects of our present political and cultural reality that underline the need for a prominent, persistent, and intellectually honest airing of our linguistic dirty laundry, and the mainstream press is our best hope for getting it.


Thank you!

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