Recognizing Stances in Online Debates - PowerPoint Presentation

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Recognizing Stances in Online Debates - PPT Presentation

Debate iPhone vs Blackberry iPhone of course Blackberry is now for the senior businessmen market The iPhone incarnate the 21st century whereas Blackberry symbolizes an outdated technology The iPhone can reach a very diversified clientele ID: 532571

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Slide1

Recognizing Stances in Online Debates

Debate: iPhone vs. BlackberryiPhone of course. Blackberry is now for the senior businessmen market! The iPhone incarnate the 21st century whereas Blackberry symbolizes an outdated technology. The iPhone can reach a very diversified clientele …

ACL 2009 swapna@cs.pitt.eduSlide2

Recognizing Stances in Online Debates

ACL 2009 swapna@cs.pitt.edu

Side Classification: pro-iPhone stanceSlide3

Recognizing Stances in Online Debates

Debate: iPhone vs. Blackberry

iPhone

of course

. Blackberry is now for the senior businessmen market!

The

iPhone

incarnate the 21st century

whereas Blackberry symbolizes an outdated technology.

The iPhone can reach a very diversified clientele …

ACL 2009 swapna@cs.pitt.edu

Arguing why their stance is correctSlide4

Recognizing Stances in Online Debates

Debate: iPhone vs. Blackberry

iPhone

of course.

Blackberry is now for the senior businessmen market!

The

iPhone

incarnate the 21st century whereas

Blackberry symbolizes an outdated technology

. The iPhone can reach a very diversified clientele …

ACL 2009 swapna@cs.pitt.edu

Justifying why the opposite side is not goodSlide5

ACL 2009 swapna@cs.pitt.edu

http://www.convinceme.net/Slide6

ACL 2009 swapna@cs.pitt.edu

http://www.convinceme.net/

Side Classification: pro-iPhone stance

Side Classification: pro-Blackberry stance

Side Classification: pro-iPhone stance

Topics:

iPhone

Blackberry

Sides/ Stances:

Pro-iPhone

Pro-Blackberry

Dual-topic,

Dual-sided debates regarding Named EntitiesSlide7

Goal

Debate stance recognition using opinion analysis

Learn debating preferences from the web

Exploited in an unsupervised approach

Combines the individual pieces of information to classify the overall stance

ACL 2009 swapna@cs.pitt.eduSlide8

Challenges

ACL 2009 swapna@cs.pitt.eduThe iPhone incarnate the 21st century whereas Blackberry symbolizes an outdated technologySlide9

Challenges

ACL 2009 swapna@cs.pitt.eduThe iPhone incarnate the 21st century whereas Blackberry symbolizes an

outdated technology

Positive

and

negative

opinions are employed to argue for a side

Side Classification: pro-iPhone stanceSlide10

Challenges

ACL 2009 swapna@cs.pitt.eduThe iPhone incarnate the 21st century

whereas Blackberry symbolizes an outdated technology

Positive

and

negative

opinions are employed to argue for a side

Opinions towards both

topics

within a post

Side Classification: pro-iPhone stanceSlide11

Challenges

ACL 2009 swapna@cs.pitt.eduThe iPhone incarnate the 21st century

whereas Blackberry symbolizes an outdated technology

Positive

and

negative

opinions are employed to argue for a side

Opinions towards both

topics

within a post

Side Classification: pro-iPhone stance

towards iPhone- towards BlackberrySlide12

Challenges

ACL 2009 swapna@cs.pitt.eduThe iPhone incarnate the 21st century

whereas Blackberry symbolizes an outdated technology

Positive

and

negative

opinions are employed to argue for a side

Opinions towards both

topics

within a post

Side Classification: pro-iPhone stance

towards iPhone- towards Blackberry

We need to consider not only positive and negative opinions

but also what they are about (targets)Slide13

Challenges

Pro-blackberryThe Pearl does music and video nicely …

First, you still can't beat the

full QWERTY keyboard

for quick, effortless typing.

Pro-iPhone

Well,

Apple has always been a well known company

Its MAC OS

is also a unique thing.ACL 2009 swapna@cs.pitt.eduSlide14

Challenges

Pro-blackberryThe Pearl does music and video nicely …

First, you still can't beat the

full QWERTY keyboard

for quick, effortless typing.

Pro-iPhone

Well,

Apple has always been a well known company

Its MAC OS

is also a unique thing.Debate topics are evoked in a variety of ways: Opinions explicitly toward the named topics are not enoughType of Blackberry

Feature of Blackberry

Maker of iPhone

Feature of iPhone

ACL 2009 swapna@cs.pitt.eduSlide15

Challenges

Pro-blackberryThe Pearl does music and video nicely …

First, you still can't beat the

full QWERTY keyboard

for quick, effortless typing.

Pro-iPhone

Well,

Apple has always been a well known company

Its MAC OS

is also a unique thing.We need to consider not only opinions towards topics,But also opinions towards aspectsACL 2009 swapna@cs.pitt.eduSlide16

Challenges

Pro-blackberryThe Pearl does music and video nicely …

First, you still can't beat the

full QWERTY keyboard

for quick, effortless typing.

Pro-iPhone

Well,

Apple has always been a well known company

Its MAC OS

is also a unique thing.We need to consider not only opinions towards topics,But also opinions towards aspects

Unique Aspects

ACL 2009 swapna@cs.pitt.eduSlide17

Challenges

iPhone and Blackberry, bothHave e-mail facilitiesCan be used to take photosOperate on batteries

Etc.Both sides share aspects

ACL 2009 swapna@cs.pitt.eduSlide18

Challenges

… I love the ability to receive emails from my work account…ACL 2009 swapna@cs.pitt.edu

People expressing positive opinions regarding emails (generally) prefer BlackberrySlide19

Challenges

… I love the ability to receive emails from my work account…ACL 2009 swapna@cs.pitt.edu

Certain shared aspects may be perceived to be better in one side

Email on Blackberry

Value for shared aspects depends on personal preferences

Emailing – pro-Blackberry people will argue via Email+

That is, Email+ is often a strategy for arguing for the pro-Blackberry stance.

Or, Browsing+ for

iPhoneSlide20

Challenges

… I love the ability to receive emails from my work account…ACL 2009 swapna@cs.pitt.edu

We need to find what a preference/dislike for an individual target means towards the debate stance as a whole Slide21

Challenges

While the iPhone looks nice and does play a decent amount of music, it can't compare in functionality to the BB.

Concessionary opinions can be misleading

ACL 2009 swapna@cs.pitt.edu

Side Classification: pro-Blackberry stanceSlide22

Challenges

While the iPhone looks nice and does play a decent amount of music, it can't compare in functionality to the BB.

ACL 2009 swapna@cs.pitt.edu

Side Classification: pro-Blackberry stance

We need to detect and handle concessionary opinionsSlide23

Challenges: Summary

For debate stance recognition we need to:Consider not only positive and negative opinions, but also what they are about (targets).Consider not only opinions towards topics, but also opinions towards aspectsFind what a preference/dislike for an individual target means towards the debate stance as a wholeDetect and handle concessionary opinions

ACL 2009 swapna@cs.pitt.eduSlide24

Challenges: Summary

ACL 2009 swapna@cs.pitt.edu

Turney

, 2002; Pang et al, 2002; Dave et al, 2003; Yu and

Hatzivassiloglou

, 2003, Pang and Lee 2005, Wilson et al 2005, Goldberg and Zhu, 2006, Kim and

Hovy

2006 …Slide25

Challenges: Summary

ACL 2009 swapna@cs.pitt.edu

and Liu, 2004; Popescu and

Etzioni

., 2005; Bloom et al. 2007,

Stoyanov

and

Cardie

2008;

Xu et al., 2008 …Slide26

For debate stance recognition we need to:

Consider not only positive and negative opinions, but also what they are about (targets).Consider not only opinions towards topics, but also opinions towards aspectsFind what a preference/dislike for an individual target means towards the debate stance as a wholeDetect and handle concessionary opinions

Our Approach

ACL 2009 swapna@cs.pitt.eduSlide27

For debate stance recognition we need to:

Our Approach

ACL 2009 swapna@cs.pitt.edu

Adopting from previous work,

Opinion-target pairing using Opinion Lexicons and Syntactic rules Slide28

For debate stance recognition we need to:

Our Approach

ACL 2009 swapna@cs.pitt.edu

Unsupervised system

Learn Associations from web and incorporate these towards stance recognition

Adopting from previous work,

Opinion-target pairing using Opinion Lexicons and Syntactic rules Slide29

For debate stance recognition we need to:

Our Approach

ACL 2009 swapna@cs.pitt.edu

Unsupervised system

Learn Associations from web and incorporate these towards stance recognition

Adopting from previous work,

Opinion-target pairing using Opinion Lexicons and Syntactic rules

Rule-based Concession Handler using PDTB connectivesSlide30

Methodology

Learn associations from web data (weblogs)Process the web data to Find opinion-target pairsAssociate opinion-target pairs with each debate side Utilize the associations to classify debate postsProcess the

debate posts toFind opinion-target pairs in the postHandle concessionary opinions

Optimize over all opinion-targets for a post-level stance classification

ACL 2009 swapna@cs.pitt.eduSlide31

Methodology

Find opinion-target pairs in the postHandle concessionary opinionsOptimize over all opinion targets for a post-level stance classification

ACL 2009 swapna@cs.pitt.eduSlide32

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.eduDebate title

Topic1 = iPhoneTopic2 = BBSlide33

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Yahoo search engine APISlide34

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topics

Yahoo search engine APISlide35

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Stanford parserSlide36

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Parsed web documents

Stanford parserSlide37

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Parsed web documents

Opinion-target pairingSlide38

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Parsed web documents

Opinion-target pairing

Lexicon

like:

hate:

Wilson et al., 2005Slide39

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Parsed web documents

Opinion-target pairing

Lexicon

Syntactic Rules

like = +

hate = -Slide40

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Parsed web documents

Opinion-target pairing

Lexicon

Syntactic Rules

I like email =

email+

like = +

hate = -Slide41

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Parsed web documents

Opinion-target pairing

Lexicon

Syntactic Rules

I like email =

email+

Associations with topic-polarity

like = +

hate = -Slide42

Topic1+

Topic1-

Topic2-

Topic2+

target

what does a positive opinion towards a target mean with respect to positive or negative opinions regarding either of the topics

Associations with topic-polarity

ACL 2009 swapna@cs.pitt.eduSlide43

Associations with topic-polarity

For each opinion-topic pair (topic1+, topic1-, topic2+, and topic2-) found in the web documentFind other opinion target pairs (target

jp) in its vicinityFor each opinion-target (targetj

) calculate its association with each of the opinion-topics

P(topic

+|targetj+) P(topic1-|target

P(topic2+|targetj+) P(topic2-|targetj+) ACL 2009 swapna@cs.pitt.edu

P(iPhone+ |email+)

P(iPhone- |email+)P(BB+ |email+)

P(BB- |email+)Slide44

Methodology: Learning associations

ACL 2009 swapna@cs.pitt.edu

Web search engine

Debate title

Topic

= iPhone

Topic

= BB

Weblogs containing both topicsParser

Parsed web documents

Opinion-target pairing

Lexicon

Syntactic Rules

I like email =

email+

Associations with topic-polarity

P(iPhone- |email+)

P(BB- |email+)

P(iPhone+ |email+)

P(BB+ |email+)

like = +

hate = -Slide45

Blackberry+

Blackberry-

iPhone

Storm-

0.062

0.843

0.06

0.03

Associations with topic-polarity

ACL 2009 swapna@cs.pitt.eduSlide46

Methodology

Find opinion-target pairs in the postHandle concessionary opinionsOptimize over all opinion targets for a post-level stance classification

ACL 2009 swapna@cs.pitt.eduSlide47

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Debate PostSlide48

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate PostSlide49

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target pairing in the post

Lexicon

Syntactic Rules

I like email =

email+Slide50

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target pairing in the post

Lexicon

Syntactic Rules

I like email =

email+

Association lookup, Side Mapping Slide51

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target pairing in the post

Lexicon

Syntactic Rules

I like email =

email+

Association lookup, Side Mapping

P(iPhone- |email+)

P(BB- |email+)

P(iPhone+ |email+)

P(BB+ |email+)Slide52

Topic1+

Topic1-

Topic2-

Topic2+

target+

Association of positive opinion towards a target to positive or negative opinions regarding either of the topics

Association Lookup

0.1

0.05

0.5

0.35

ACL 2009 swapna@cs.pitt.eduSlide53

Side-1

Side-2

Topic1+

Topic1-

Topic2-

Topic2+

target+

Side-1 = Topic1+ alternatively Topic2-

Side-2 =Topic2+ alternatively Topic1-

Association Lookup, Side Mapping

0.1

0.05

0.5

0.35

ACL 2009 swapna@cs.pitt.eduSlide54

target+

Side-1

Side-2

0.15

0.85

Association of positive opinion towards a target to both of the stances

Association Lookup, Side Mapping

ACL 2009 swapna@cs.pitt.eduSlide55

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target

pairingin

the post

Lexicon

Syntactic Rules

I like email =

email+

Association lookup, Side Mapping

P(iPhone- |email+)

P(BB- |email+)

P(iPhone+ |email+)

P(BB+ |email+)Slide56

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target

pairingin

the post

Lexicon

Syntactic Rules

I like email =

email+

Association lookup, Side Mapping

P(iPhone- |email+)

P(BB- |email+)

P(iPhone+ |email+)

P(BB+ |email+)

Assoc(Side-1, email+)

Assoc(Side-2, email+)Slide57

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target pairing in the post

Lexicon

Syntactic Rules

I like email =

email+

Association lookup, Side Mapping

P(iPhone- |email+)

P(BB- |email+)

P(iPhone+ |email+)

P(BB+ |email+)

Concession Handling

Assoc(Side-1, email+)

Assoc(Side-2, email+)Slide58

Concession Handling

Detecting concessionary opinions

Find Concession indicators

Discourse connectives from Penn Discourse Treebank (Prasad et al., 2007)

Use simple rules to find the conceded part of the sentence

While

the

iPhone

looks nice

and

does play a decent amount of music, it can't compare in functionality to the BB.

like my

music

, and

phone

but

don't want

carry a brick around

in my pocket when I only need my phone.

ACL 2009 swapna@cs.pitt.eduSlide59

Concession Handling

I like my music, and phone

, but I don't want to carry a brick around

in my pocket when I only need my phone.

Conceded opinions

music+

phone+

ACL 2009 swapna@cs.pitt.eduSlide60

Side-2

Pro-Iphone

Side-1

Pro-Blackberry

music+

phone+

1.0

0.509

0.45

Original associations learnt from the web

Concession Handling

ACL 2009 swapna@cs.pitt.eduSlide61

Side-2

Pro-Iphone

Side-1

Pro-Blackberry

music+

phone+

1.0

0.509

0.45

Concession Handling

Associations after concession handling

Conceded opinions are counted for the opposite side

ACL 2009 swapna@cs.pitt.eduSlide62

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target pairing in the post

Lexicon

Syntactic Rules

I like email =

email+

Association lookup, Side Mapping

P(iPhone- |email+)

P(BB- |email+)

P(iPhone+ |email+)

P(BB+ |email+)

Concession Handling

Post-level association

aggregation

Assoc(Side-1, email+)

Assoc(Side-2, email+)Slide63

Side-2

Pro-Iphone

Side-1

Pro-Blackberry

Aggregation

target

Each opinion-target pair in the post has a bias toward one or the side

0.9

0.7

0.4

0.5

0.1

0.3

0.6

0.5

ACL 2009 swapna@cs.pitt.eduSlide64

Side-2

Pro-Iphone

Side-1

Pro-Blackberry

Aggregation

target

Each opinion-target pair in the post has a bias toward one or the other side

Optimize the post classification such that

The side assigned to the post maximizes the association value of the post

ACL 2009 swapna@cs.pitt.eduSlide65

Methodology: Stance Classification

ACL 2009 swapna@cs.pitt.edu

Debate Post

Parser

Debate Post

Parsed Debate Post

Opinion-target pairing in the post

Lexicon

Syntactic Rules

I like email =

email+

Association lookup, Side Mapping

P(iPhone- |email+)

P(BB- |email+)

P(iPhone+ |email+)

P(BB+ |email+)

Concession Handling

Post-level association

aggregation

Side=

pro-Topic1

Assoc(Side-1, email+)

Assoc(Side-2, email+)Slide66

Blackberry+

Blackberry-

iPhone

Storm-

0.062

0.843

0.06

0.03

Associations learnt from web data

ACL 2009 swapna@cs.pitt.eduSlide67

Blackberry+

Blackberry-

iPhone

Storm+

0.227

0.068

0.022

0.613

Associations learnt from web data

Both

OpPMI

, and Op-

Pref

agree with each other;

Both learnt the IS-A relationship

ACL 2009 swapna@cs.pitt.eduSlide68

Blackberry+

Blackberry-

iPhone

Keyboard+

0.718

0.0

0.12

0.09

Associations learnt from web data

ACL 2009 swapna@cs.pitt.eduSlide69

Blackberry+

Blackberry-

iPhone

Keyboard-

0.25

0.125

0.375

Associations learnt from web data

Negative opinions towards keyboards are not useful for side discrimination

0.5

ACL 2009 swapna@cs.pitt.eduSlide70

Summing Up

Looked at several tasks ranging from purely lexical to discourse classification

Identify subjective words

Classify their senses as subjective or objective

Recognize, in a text or conversation, whether a word is used with a subjective or objective sense

Sense-aware contextual subjectivity and sentiment analysis

Contextual polarity recognition

Discourse-Level Opinion Interpretation

Many ambiguities are involved in interpreting subjective language!Slide71

Summing Up

Many other ambiguities than these!Sarcasm and IronyYeah, he’s just wonderful.You’re no different from the mob! Oh, there’s a big difference, Mrs. De Marco. The mob is run by murdering, thieving, lying, cheating psychopaths. We work for the President of the United States.

[Married to the Mob]Literal versus non-literal language

He is a pain in the neckSlide72

Pointers

Please see http://www.cs.pitt.edu/~wiebe Publications OpinionFinder

Subjectivity lexicon MPQA manually annotated corpus

Tutorials

Bibliography Slide73

Acknowledgements

Subjectivity Research Group, Pittsburgh

Cem Akkaya, Yaw Gyamfi, Paul Hoffman, Josef Ruppenhofer, Swapna Somasundaran, Theresa Wilson

Cornell:

Claire Cardie, Eric Breck, Yejin Choi, Ves Stoyanov