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

From Sentiment to Persuasion
Analysis: A Look at Idea Generation
Tools
Jason Kessler
Data Scientist, CDK Global
@jasonkessler
www.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

Cruze…
Rating: 4.4/5 Stars
Automotive Site
# of users who
read review:
# who went on to
visit a Chevy
dealer’s website: 15
20
Text:

Cruze…
Rating: 4.4/5 Stars
Automotive Site
# of users who
read review:
# who went on to
visit a Chevy
20
Review Engagement Rate:
15/20=75%
Text:

Cruze…
Rating: 4.4/5 Stars
Automotive Site
# of users who
read review:
# who went on to
visit a Chevy
20
Review Engagement Rate:
15/20=75%
Text:
Median Review Engagement Rate:
22%

Positive Sentiment High Engagement
Love Comfortable
Comfortable Front [Seats]
Features Acceleration
Solid Free [Car Wash, Oil Change]
Amazing Quiet
Sentiment vs. Persuasiveness: SUV-Specific

Positive Sentiment High Engagement
Love Comfortable
Comfortable Front [Seats]
Features Acceleration
Solid Free [Car Wash, Oil Change]
Amazing Quiet
Sentiment vs. Persuasiveness: SUV-Specific
Negative Sentiment Low Engagement
Transmission Money [spend my, save]
Problem Features
Issue Dealership
Dealership Amazing
Times 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 Terms
Love
Awesome
Fantastic
Handled
Perfect

Engagement Terms
Blind (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…”
- …

Can better science improve messaging?
Engagement Terms
Blind
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”

Engagement Terms
Blind
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…

Just recently, VW has produced very similar
commercials.

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,

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?

Source: http://blog.okcupid.com/index.php/page/7/ (Rudder 2010)
Words and phrases that distinguish Latino men.
Explanation
on a dating site?

Source: http://blog.okcupid.com/index.php/page/7/
Words and phrases that distinguish Latino men.
on a dating site?
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.
on a dating site?

* Not drawn to scale
Rankingwith
democrats
Ranking with
republicans
top
middle
bottom
bottom
middle
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.

Rankingwith
democrats
Ranking with
republicans
top
middle
bottom
bottom
middle
top
giraffe✚
olympics
✚
ann
✚
bipartisan ✚
people✚
stand ✚
election ✚auto✚
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

Rankingwith
democrats
Ranking with
republicans
top
middle
bottom
bottom
middle
top
giraffe✚
olympics
✚
ann
✚
bipartisan ✚
people✚
stand ✚
election ✚auto✚
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 Terms
Included in
Visualization? Comment
olympics no
Gov. Romney was CEO of the Organizing Committee for the 2002 Winter
Olympics.
ann no Ann Romney
big government no
16 [trillion] no Size of national debt
oklahoma no Speech by Mary Fallin, OK governor, mentioned state numerous times.
elect mitt yes
next president no
the constitution no Mostly referring to allegedly unconstitutional actions by Pres. Obama
mitt 's yes
our 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] no
8% 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 no
Discussions 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?
Before
After

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 Terms
Included 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 no
woman '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 care
millionaire yes
platform no Repubs never mentioned party platform
voucher 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

• 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
from text
Top 5 high revenue terms (Rudder algorithm)
Review-level observations Movie-level observations
Batman Computer generated
Borat Superhero
Rodriguez The franchise
Wahlberg Comic book
Comic book Popcorn

from text
Top 5
Computer generated
Superhero
The franchise
Comic book
Popcorn
Bottom 5
exclusively
[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 movies
Selected Top Terms
Franchise
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 over time in Facebook statuses
Best 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 cons
Alternative 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’s
Language
Visualization
Tool

• 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.com
-or-
talk to me
@jasonkessler

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