From Sentiment to Persuasion Analysis: A Look at Idea Generation Tools

From Sentiment to Persuasion Analysis: A Look at Idea Generation ToolsJason KesslerData Scientist, CDK Global

@jasonkesslerwww.jasonkessler.com

Outline• Idea generation tools

– Use large corpora to generate hypotheses about questions like:– How do you make a persuasive ad? – How can presidential candidates improve their rhetoric?– How do ethnicity and gender correlate to language use in online

dating profiles? – How do movie reviews predict box-office success?

• Technical content:– Ways of extracting category-associated words and phrases from

corpora– UX around displaying and provided context to associated words and

phrases

Customer-Written Product Reviews

Good Ad Content

Naïve Approach: Indicators of Positive Sentiment

"If you ask a Subaru owner what they think of their car, more times than not they'll tell you they love it," -Alan Bethke, director of marketing communications for Subaru of America (via Adweek)

Positive sentiment.

Engaging language.

Finding Engaging Content

…I was very skeptical giving up my truck and buying an "Economy Car." I'm 6' 215lbs, but my new career has me driving a personal vehicle to make sales calls. I am overly impressed with my Cruze…

Rating: 4.4/5 Stars

Example Review Appearing on a 3rd Party Automotive Site

# of users who read review: 20

Text:Car Reviewed: Chevy Cruze



Rating: 4.4/5 Stars


# of users who read review:

# who went on to visit a Chevy dealer’s website: 15

20Text:Car Reviewed: Chevy Cruze



Rating: 4.4/5 Stars




20

Review Engagement Rate:

15/20=75%Text:Car Reviewed: Chevy Cruze



Rating: 4.4/5 Stars




20

Review Engagement Rate:

15/20=75%Text:Car Reviewed: Chevy Cruze

Median Review Engagement Rate:

22%

Positive Sentiment High EngagementLove ComfortableComfortable Front [Seats]Features AccelerationSolid Free [Car Wash, Oil Change]Amazing Quiet

Sentiment vs. Persuasiveness: SUV-Specific

Positive Sentiment High EngagementLove ComfortableComfortable Front [Seats]Features AccelerationSolid Free [Car Wash, Oil Change]Amazing Quiet

Sentiment vs. Persuasiveness: SUV-Specific

Negative Sentiment Low EngagementTransmission Money [spend my, save]Problem Features

Issue DealershipDealership AmazingTimes Build Quality [typically positive]

• We’ll discuss algorithms later in the talk• Basically, we rank words and phrases based on their classifier

produced feature weights• Techniques and technologies used

– Unigram and bigram features (bigrams must pass a simple key-phrase test)

– Ridge classifier

Algorithm for finding word lists

High Sentiment TermsLoveAwesome

Fantastic

Handled

Perfect

Engagement TermsBlind (spot, alert)

Contexts from high engagement reviews- “The techno safety features

(blind spot, lane alert, etc.) are reason for buying car...”

- “Side blind Zone Alert is truly wonderful…”

- …

BLIND SPOT ALERT.

Can better science improve messaging?

BLIND SPOT ALERT.

Engagement TermsBlind

White (paint, diamond)

Contexts

- “White with cornsilk interior.”- “My wife fell in love with the

Equinox in White Diamond”- “The white diamond paint is

to die for”


BLIND SPOT ALERT.


BLIND SPOT ALERT.

Engagement TermsBlind

White

Climate (geography, a/c)

Contexts- “Love the front wheel drive

in this northern Minn. Climate”

- “We do live in a cold climate (Ontario)”

- …climate control…

BLIND SPOT ALERT.

Just recently, VW has produced very similar commercials.

Process

Process

Corpus collection

Label documents with class of

interest

Find linguistic elements that are associated with

class

Explain why linguistic

elements are associated.

Identify documents of interest.

• For CDK’s usage:• Persuasive

• High engagement rate.

• Positive• High star rating.

- Show representative contexts.

- Human generated explanation.

- Statistics supporting association.

- Ideation.

Complicated!

Will be a major focus of this talk.

Case Study 1:Language of Politics

NYT: 2012 Political Convention Word Use by Party

Mike Bostock et al., http://www.nytimes.com/interactive/2012/09/06/us/politics/convention-word-counts.html

2012 Political Convention Word Use by Party

Source: http://www.nytimes.com/interactive/2012/09/06/us/politics/convention-word-counts.html,

http://www.nytimes.com/interactive/2012/09/06/us/politics/convention-word-counts.html




Corpus has a class size imbalance:- Democrats: 79k words across 123 speeches- Republicans: 60k words across 66 speeches

“Number of mentions by spoken words” - Normalizes imbalance (282 vs. 182)- More understandable than P(jobs|Democrat) vs.

P(jobs|Republican), which are both extremely low numbers (0.36% vs. 0.30%)

• Corpus: Political Convention Speeches• Class labels: Political Party of Speaker• Linguistic elements:

– Words and phrases– Manually chosen

• Explanation:– Cool bubble diagram– Selective topic narration– Click-to-view topic contexts organized by speaker and party

• We’ll get back to this in a minute

Summary: NYT 2012 Conventions

Case Study 2:Language of Self-Representation

OKCupid: How does gender and ethnicity affect self-presentation on online dating profiles?

Christian Rudder: http://blog.okcupid.com/index.php/page/7/

Which words and phrases statistically distinguish ethnic groups and genders?

hobos

almond butter 100 Years of

Solitude

Bikram yoga

Source: http://blog.okcupid.com/index.php/page/7/ (Rudder 2010)

Words and phrases that distinguish white men.

OKCupid: How do ethnicities’ self-presentation differ on a dating site?

http://blog.okcupid.com/index.php/page/7/

Source: http://blog.okcupid.com/index.php/page/7/ (Rudder 2010)

Words and phrases that distinguish Latino men.

Explanation


http://blog.okcupid.com/index.php/page/7/

Source: http://blog.okcupid.com/index.php/page/7/



latin music latin music

latin musiclatin music latin music

The explanation suggests a topic modeling may help to identify latent themes that are driving these word and phrase distinctiveness.

Source: http://blog.okcupid.com/index.php/page/7/



latin music latin music

latin musiclatin music

latin nationality

martial arts

latin music

martial artsmartial arts

latin nationality

latin nationality

spanish-language spanish-language

latin nationality

The explanation suggests a topic modeling may help to identify latent themes that are driving these word and phrase distinctiveness.

What can we do with this?• Genre of insurance or investment ads

– Montage of important events in the life of a person.

• With these phrase sets, the ads practically write themselves:

• What if you wanted to target Latino men? – Grows up boxing– Meets girlfriend salsa dancing– Becomes a Marine– Tells a joke at his wedding– Etc…

The linguistic elements were found “statistically.”

The exact method is unclear, but Rudder (2014) describes a novel method to identify statistically associated terms.

- Let’s look closely at the algorithm and see:- how it works - and how it performs on the political convention

data set.


* Not drawn to scale

Ran

king

with

dem

ocra

ts

Ranking withrepublicans

top

middle

bottom

botto

m

mid

dle

top

giraffe✚olympics

✚ann✚

bipartisan ✚

people✚stand ✚election ✚auto✚

wealthy✚

bin laden✚

regulatory✚✚

pelosi✚

rancher

grandfather ✚

public✚worker✚

regulation ✚

profit ✚

Source: Christian Rudder. Dataclysm. 2014.


Ran

king

with

dem

ocra

ts


top

middle

bottom

botto

m

mid

dle

top

giraffe✚olympics

✚ann✚

bipartisan ✚


wealthy✚

bin laden✚

regulatory✚✚

pelosi✚

rancher

grandfather ✚

public✚worker✚

regulation ✚

profit ✚

Association between democrats and “worker” is the Euclidean distance between word and top left corner



Ran

king

with

dem

ocra

ts


top

middle

bottom

botto

m

mid

dle

top

giraffe✚olympics

✚ann✚

bipartisan ✚


wealthy✚

bin laden✚

regulatory✚✚

pelosi✚

rancher

grandfather ✚

public✚worker✚

regulation ✚

profit ✚

Association between republicans and “regulation” is the Euclidean distance between word and bottom right corner


Another look at the 2012 political convention data


- The conventions let political parties reach a broad audience, and both energize their bases and reach undecided voters.

- How well do these terms capture rhetorical differences between parties?

Applying the Rudder algorithm to the 2012 data reveals a number of terms associated with a party that weren’t covered in the NYT viz.

These can uncover party talking points.


Republican Top TermsIncluded in Visualization? Comment

olympics noGov. Romney was CEO of the Organizing Committee for the 2002 Winter Olympics.

ann no Ann Romneybig government no16 [trillion] no Size of national debtoklahoma no Speech by Mary Fallin, OK governor, mentioned state numerous times.elect mitt yesnext president nothe constitution no Mostly referring to allegedly unconstitutional actions by Pres. Obamamitt 's yesour founding no Founding fathers. Talk of restoring values of founding fathers.jack no Republicans just seem to talk about people named Jack more.

8 [percent] no8% unemployment. The term “unemployment” was used in the visualization, but Democrats didn’t mention the percentage.

they just no “Just don’t get it” was a refrain of a Repub. speaker.

patient noDiscussions of US being “patient,” as well as how the ACA affects the doctor-patient relationship

pipeline no Keystone pipeline

How well do these terms capture linguistic differences between parties?


BeforeAfter

Now let’s look at the Democrats.• The auto bailout is Pres. Obama’s 2012 Olympics.• Government is seen as a collection of programs (Pell grants, Medicare

Vouchers, etc…) to help middle class families, vs. “big government”.• Attacks on wealthy• No appeals to fundamental principles (“constitution,” “founding fathers”)• Women explicitly mentioned, while Repubs. talk about Ms. Romney.


Democratic Top TermsIncluded in Visualization? Comment

auto [industry] yes Provided in NYT. Pres. Obama was credited with auto industry recovery.[move] america forward yes *only “forward” was included in visualization. insurance company nowoman 's yes[the] wealthy no Never used by Repubs.pell [grant] no Never used by Repubs.last week no Used to talk about RNC that happened the pervious week.grandmother no 6:1 ratio of Dem vs. republican usage. Dovetails with discussion of women.access no Access to gov’t services or health caremillionaire yesplatform no Repubs never mentioned party platformvoucher no Accusing Republicans of turning Medicare into “voucher.”class family yes “Middle class” was included.register no Voter registration. Only used once by Repubs.

• Democrats had an advantage in having their convention last– They could refute Republican talking points– The Republicans made Gov. Romney's role in the 2002 Olympics a

major selling point• It went virtually unmentioned by Democrats

• Republicans may be using numbers to their detriment:– 8% unemployment

• Often “for 42 months” was added

– $16 trillion deficit– These numbers are tough to interpret without a lot context

• Romney’s “47%… …are dependent on the government, believe they are victims” comment may have been the death-nail in his presidential bid

• Jeb Bush’s campaign point of “4% GDP growth” has been ineffective– His polling numbers are at about 4% at the time of this talk

How can this aid in messaging?

Case Study 3:Movie reviews and revenue

- Data:- 1,718 movie reviews from 2005-2009 7 different publications

(e.g., Austin Chronicle, NY Times, etc.)- Various movie metadata like rating and director- Gross revenue

- Task:- Predict revenue from text, couched as a regression problem- Regressor used: Elastic Net

- l1 and l2 penalized linear regression- 2009 reviews were held-out as test data

- Linguistic elements:- Ngrams: unigrams, bigrams and trigrams- Dependency relation triples: <dependent, relation, head>- Versions of features labeled for each publication (i.e. domain)

- “Ent. Weekly: comedy_for”, “Variety: comedy_for”- Essentially the same algo as Daume III (2007)

- Performed better than naïve baseline, but worse than metadata

Predicting Box-Office Revenue From Movie Reviews

Joshi et al. Movie Reviews and Revenues: An Experiment in Text Regression. NAACL 2010 Daume III. Frustratingly Easy Domain Adaptation. ACL 2007.


Joshi et al. Movie Reviews and Revenues: An Experiment in Text Regression. NAACL 2010

manually labeled feature categories

Feature weight (“Weight ($M)”) in linear model indicates how much features are “worth” in millions of dollars.

The learned coefficients.

- 2015 follow-up work: - Using convolutional neural network in

place of Elastic Net

Bitvai and Cohn: Non-Linear Text Regression with a Deep Convolutional Neural Network. ACL 2015


Bitvai and Cohn: Non-Linear Text Regression with a Deep Convolutional Neural Network.

- Word association for convolutional neural network regressor

- Algorithm: - Compare the prediction of the regressor

with phrase zeroed out in input to original output.

- Impact is the difference in outputs.- Impact for “Hong Kong” will involve

running regressor with “Hong Kong” zeroed out in movie representation, but unigrams “Hong” and “Kong” are unaffected.

Impact = predict({…, “Hong Kong”: 1, …}) – predict({…, “Hong Kong”: 0, …})


Bitvai and Cohn: Non-Linear Text Regression with a Deep Convolutional Neural Network.

- Explanation- ‘#’s reflect count of movies in test set

having a review that used phrase- Min is lowest impact across movies

classified, max is highest, used for ordering top positive and negative.

- Many “top” terms only appear in one movie.

- Manually selected phrases are ordered by the average impact.

- Open questions:- Does the increase or decrease in the

prediction actually improve the regressor’s performance?

- Including the average decrease in MAE among movies with phrase would address this.

• The corpus used in Joshi et al. 2010 is freely available.• Can we use the Rudder algorithm to find interesting associated

terms? How does it compare?– Rudder algorithm requires two or more classes.– We can partition the the dataset into high and low revenue partitions.

• High being movies in the upper third of revenue• Low in the bottom third

– Find words that are associated with high vs. low (throwing out the middle third) and vice versa

Univariate approach to predicting revenue from text

• Observation definition is really important!– Recall that the same movie may have multiple reviews.– We can treat an observation as

• a single review• a single movie

– The response variable remains the same– movie revenue

Univariate approach to predicting revenue-category from text





Top 5 high revenue terms (Rudder algorithm)Review-level observations Movie-level observationsBatman Computer generated

Borat Superhero

Rodriguez The franchise

Wahlberg Comic book

Comic book Popcorn





Overfi

t!

Top 5 high revenue terms (Rudder algorithm)Review-level observations Movie-level observationsBatman Computer generated

Borat Superhero

Rodriguez The franchise

Wahlberg Comic book

Comic book Popcorn


Top 5Computer generated

Superhero

The franchise

Comic book

Popcorn

Bottom 5exclusively

[Phone number]

Festival

Tribeca

With English

Failed to produce term associations around content ratings (e.g., PG-13, “strong language”). Rating is strongly correlated to revenue.

Let’s look exclusively at PG-13 movies

Only PG-13-rated moviesSelected Top TermsFranchise

Computer generated

Installment

The first two

The ultimate

Selected Bottom Terms[Theater specific terms like phone numbers]A friend

Her mother

Parent

One day

Siblings

Top terms are very similar. Franchises and sequels remain indicator of success.

Bottom terms tell us something new!

Movies about friendship or family dynamics don’t seem to perform well.

Idea generation tools can also be idea rejection tools. - Producers looking for a movie to pull in a lot of revenue, a PG-13 family melodrama isn’t a great idea.

Lesson: Corpus selection is important in getting actionable, interpretable results!

Language use and age

Language use over time in Facebook statusesBest topic for each age group listed.

LOESS regression line for prevalence by age group

Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, et al. (2013) Personality, Gender, and Age in the Language of Social Media: The Open Vocabulary Approach. PLoS ONE 8(9)

Nod to James Pennebaker

Word cloud pros and consAlternative to word cloud is list, ranked by phrase frequency or phrase precision.

Pro• Word clouds force you to

hunt for the most impactful terms

• You end up examining the long tail in the process

• Compactly represent many phrases

Con• Longer words are more

prominent.• “Mullet of the Internet”• Hard to show phrase

annotations.• Ranking is unclear.

Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, et al. (2013) Personality, Gender, and Age in the Language of Social Media: The Open Vocabulary Approach. PLoS ONE 8(9)

CDK Global’sLanguageVisualizationTool

• Suppose you are selling a car to a typical person, how would you describe the car’s performance?

• Should you say– This car has 162 ft-lbs of torque.– OR– This car makes passing on two lane roads easy.

• Having an idea generation (and rejection) tool makes this very easy.

Informing dealer talk tracks.

• Corpus and document selection are important– Documents: movie-level instead of review-level– Corpus: rating-specific– Subsets of corpus can be particularly interesting: e.g., PG-13 movies

• Don’t always look at extreme terms– The Rudder algorithm on the NYT visualization lacked many important

issues like Medicar

• Use a variety of approaches– Univariate and multivariate approaches can highlight different terms

• More phrase context is better than less

• Phrase lists are most understandable when presented with a narrative, even if it’s a bit speculative

Recommendations

• Thank you!

• We’re hiring– talk to me (best) or, if you can’t, go to CDKJobs.com

• Special thanks Joel Collymore (the concept of “idea generation tool”), Michael Mabale (thoughts on word clouds), Michael Eggerling, Ray Littell-Herrick, Peter Huang, Peter Kahn, Iris Laband, Kyle Lo, Chris Mills, Dengyao Mo, Keith Zackarone

Acknowledgements

Questions? (Yes, we’re hiring!!)

• Data Scientist• UI/UX Development

& Design• Software Engineer –

all levels• Product Manager

Is this you?

• Find “Jobs by Category”

• Click Technology

• Have your Resume ready

• Click “Apply”!

Head to CDKJobs.co

m-or-

talk to me

@jasonkessler

From Sentiment to Persuasion Analysis: A Look at Idea Generation Tools

Software

Transcript of From Sentiment to Persuasion Analysis: A Look at Idea Generation Tools