Turning Unstructured Content into Kernels of Ideas

Turning Unstructured Content into
Kernels of Ideas
Jason Kessler
Data Scientist, CDK Global
Data Day Seattle, 2016
@jasonkessler
www.jasonkessler.com
Code used in talk:
https://github.com/JasonKessler/scattertext

1. Case studies on
mining documents
for knowledge
2. Old, new, and
obscure algorithms
and tools

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)

But does positivity
imply engagement?

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%

Are words and
phrases more
precise predictors?

Positive Sentiment
Love
Comfortable
Features
Solid
Amazing
Sentiment vs. Persuasiveness: SUV-Specific
Negative Sentiment
Transmission
Problem
Issue
Dealership
Times

Positive Sentiment High Engagement
Love Comfortable
Comfortable Front [Seats]
Features Acceleration
Solid Free [Car Wash, Oil Change]
Amazing Quiet
Negative Sentiment
Transmission
Problem
Issue
Dealership
Times

Positive Sentiment High Engagement
Love Comfortable
Comfortable Front [Seats]
Features Acceleration
Solid Free [Car Wash, Oil Change]
Amazing Quiet
Negative Sentiment Low Engagement
Transmission Money [spend my, save]
Problem Features
Issue Dealership
Dealership Amazing
Times Build Quality [typically positive]

Negative Sentiment Low Engagement
Transmission Money [spend my, save]
Problem Features
Issue Dealership
Dealership Amazing
Times Build Quality [typically positive]
• The worst thing to say when reviewing an SUV:
“I saved money and got all
these amazing features!”

Milquetoast positivity
Persuasiveness

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.

Finding useful class-
differentiating terms
is complicated.
But simple methods
work well.

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: Christian Rudder. Dataclysm. 2014.
Ranking with everyone else
The smaller the distance from the top left, the
higher the association with white men
High distance: but
ignore K-Pop
Low distance: white guys
disproportionately like Phish

Source: Christian Rudder. Dataclysm. 2014.
my blue eyes

The Rudder Score:
|| < 1,0 > − < Φ(# 𝑤𝑜𝑟𝑑,¬𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦),Φ (# 𝑤𝑜𝑟𝑑,𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦) > ||2
The percentile function Φ ranks counts along a scale of [0,1],

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

How can we make money 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 good:
• Word clouds force you
to hunt for the most
impactful terms
• You end up examining
the long tail in the
process
• Compactly represent a
lot of phrases
Lyle Ungar 2013 AAAI Tutorial
Above: words found in Facebook status message predictive of women authors.

Lyle Ungar 2013 AAAI Tutorial
Above: words found in Facebook status message predictive of male authors.
The bad:
• “Mullets of the Internet”
--Jeffrey Zeldman
• Longer phrases are are
more prominent
• Ranking is unclear
• Does size indicate
higher frequency?

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

Predicting a movie’s
revenue from its
reviews.

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

Predicting Box-Office Revenue From Movie Reviews
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.

• 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 (concatenation of all reviews)
• word frequencies: # observations words appear in
– The response variable remains the same– movie revenue

• 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 (concatenation of all reviews)
• word frequencies: # observations words appear in
– The response variable remains the same– movie revenue
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

Univariate approach to predicting revenue-category
from text
Top 5 (all movies)
Computer generated
Superhero
The franchise
Comic book
Popcorn
Bottom 5 (all movies)
exclusively
[Phone number]
Festival
Tribeca
With English
Failed to produce term associations around
content ratings (e.g., “g-rated” or “adult).
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
A friend
Her mother
Parent
One day
Siblings
Top terms are very similar. Franchises
and sequels are very successful.
Bottom terms are interesting!
Movies about friendship or family
dynamics don’t seem to perform well!
Idea generation tools can also be idea
rejection tools.
- Spiderman 15 >> PG-13 family
melodrama.
Corpus selection is important in getting
actionable, interpretable results!

Deep dive on term-
importance
algorithms

The code for these experiments are available: https://github.com/JasonKessler/scattertext/
• 138k words total. By modern standards, a pretty small corpus.
– 78k words spoken by Democrats, and 58k by Republicans
– The Brown Corpus (1961) is 1mm words
– But, often you have small amounts of data
• We can compare the word frequencies against a list of English
word frequencies.
• We’ll look at three methods to do this:
– Statistical: Fisher’s Exact Test
– Geometirc: Rudder Scores
– Information retrieval: F-Score
What terms are characteristic the 2012 conventions?

• 1. Hypothesis testing: comparing word, class frequencies in a 2x2
contingency table using Fisher’s Exact test.
Convention
Speeches
General English
Frequencies
”Romney” count 570 695,398
~” Romney” count 137,966 - 570 369 billion - 695,398
Fisher’s Exact test:
Odds ratio: 3508.61
P: 0.0
- Numerical issues can cause p-values to be zero, wreaking havoc
on Bonferroni corrections.
- Rank by p-values, and break ties on odds ratios.

• Frequencies in convention
dataset are listed next to term
• Fisher is sorted by p-value, then
OR (mostly p = 0 here)
• Fisher’s test selects
characteristic terms, but includes
stop words
• The Rudder score selects for
terms not seen elsewhere, like
names, neologisms and foreign
words
Top Fisher Terms Top Rudder Terms
obama (702) obamacare* (33)
barack (248) romneys* (8)
romney (570) yohana* (3)
mitt (501) nevadan* (2)
republican (228) solyndra* (2)
president (1049) breth* (2)
thank (366) outeducate* (2)
america (689) vouchercare* (2)
u (529) bendiga* (2)
american (653) gjelsvik* (2)
be (4298) carlette* (1)
we (2464) valora* (1)
* term was not found in background corpus

• Characteristic terms appear a lot in convention speeches
– In other words, have a high recall-- a high P(term | convention
speech)
– However, so do a lot of stop words
• If you encounter a characteristic term, there’s a good chance
you’re reading a convention speech
– Have a high precision– a high P(convention speech|term)
– However, this may be true for random low-frequency words
• Because of the two caveats, both probabilities need to be “high”.
• F-score = HarmoicMean(P(term|category), P(category|term))
Intuition behind F-Score

F-score looks pretty good here
Top Fisher Terms Top Rudder Terms Top F-score Terms
obama (702) obamacare* (33) obama (702)
barack (248) romneys* (8) romney (570)
romney (570) yohana* (3) mitt (501)
mitt (501) nevadan* (2) barack (248)
republican (228) solyndra* (2) republican (228)
president (1049) breth* (2) bless (132)
thank (366) outeducate* (2) president (1049)
america (689) vouchercare* (2) democrat (127)
u (529) bendiga* (2) elect (102)
american (653) gjelsvik* (2) biden (46)
be (4298) carlette* (1) governor (172)
we (2464) valora* (1) obamacare* (33)

@jasonkessler
Using F-Score to Find Differences between
Democratic and Republican Speakers
F-Score
the (3399:2531)
and (2707:2232)
to (2337:1664)
be (2326:1972)
a (1596:1346)
of (1565:1372)
that (1400:1051)
we (1318:1146)
• When class-label distributions are about equal, term
recalls become far lower than term precisions.
Top democratic speech predictors
(dem:rep frequencies)

@jasonkessler
F-Score
the (3399:2531)
and (2707:2232)
to (2337:1664)
be (2326:1972)
a (1596:1346)
of (1565:1372)
that (1400:1051)
we (1318:1146)
• P(the|dem) ~= P(the) ~= 0.04
• P(dem|the) ~= P(dem) ~= 0.57
• F-score = 0.073 (> 0.039)
• P(dem|auto) = 1.00
• P(auto|dem) ~= 0.0005
• F-score = 0.0009

@jasonkessler
F-Score
the (3399:2531)
and (2707:2232)
to (2337:1664)
be (2326:1972)
a (1596:1346)
of (1565:1372)
that (1400:1051)
we (1318:1146)
• P(the|dem) ~= P(the) ~= 0.04
• P(dem|the) ~= P(dem) ~= 0.57
• F-score = 0.073 (> 0.039)
• P(dem|auto) = 1.00
• P(auto|dem) ~= 0.0005
• F-score = 0.0009
• We can correct for this without having to mess with β

@jasonkessler
A new modification to F-Score
F-Score
the (3399:2531)
and (2707:2232)
to (2337:1664)
be (2326:1972)
a (1596:1346)
of (1565:1372)
that (1400:1051)
we (1318:1146)
Let Φ 𝑥 = 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 𝑥 𝑜𝑟 𝑎 𝑁𝑜𝑟𝑚𝑎𝑙𝐶𝐷𝐹(𝑥, 𝜇 𝑥, 𝜎 𝑥
2)
F−score scaled =
2Φ 𝑃(term|category) Φ 𝑃(category|term)
Φ 𝑃(term|category) + Φ 𝑃(category|term)
• Solution: normalize probabilities
• Φ (P(dem|auto)) ~= 0.99 (avg. pctl)
• Φ (P(auto|dem)) ~= 0.66
• F-score = 0.78
• Φ (P(dem|the)) ~= 0.19
• Φ (P(the|dem)) ~= 1.0
• F-score = 0.33

@jasonkessler
A new modification to F-Score
F-Score
F-Score Scaled
(NormCDF)
the (3399:2531) fight for (108:7)
and (2707:2232) auto (37:0)
to (2337:1664) fair (47:3)
be (2326:1972) insurance company (28:0)
a (1596:1346) america forward (28:0)
of (1565:1372) middle class (149:18)
that (1400:1051) president barack (47:4)
we (1318:1146) auto industry (24:0)
Let Φ 𝑥 = 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 𝑥 𝑜𝑟 𝑎 𝑁𝑜𝑟𝑚𝑎𝑙𝐶𝐷𝐹(𝑥, 𝜇 𝑥, 𝜎 𝑥
2)
F−score scaled =
2Φ 𝑃(term|category) Φ 𝑃(category|term)
Φ 𝑃(term|category) + Φ 𝑃(category|term)
• Solution: normalize probabilities
• Φ (P(dem|auto)) ~= 0.99 (avg. pctl)
• Φ (P(auto|dem)) ~= 0.66
• F-score = 0.78
• Φ (P(dem|the)) ~= 0.19
• Φ (P(the|dem)) ~= 1.0
• F-score = 0.33

I’ve written software
to create the plots
of term frequencies,
similar to Rudder
(2014).
This shows
Democratic vs.
Republican
language, colored
with Scaled F-
Scores.
The code is
available at
https://github.com/J
asonKessler/scatter
text/

Colored with L1-
penalized logistic
regression
coefficients
The code is
available at
https://github.com
/JasonKessler/sc
attertext/

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
– People who bought this car tell us it 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
• Use a variety of approaches
– Univariate and multivariate approaches can highlight different terms
• Visualize your data set– the pre-alpha text-to-ideas package can
let you do that. https://github.com/JasonKessler/text-to-ideas/
• More phrase context is better than less
• Add a narrative behind to you analysis, no matter how speculative.
Recommendations

What you learn from
corpora is often
more important
than classification
accuracy.

• Thank you!
• Check out https://github.com/JasonKessler/scattertext/ for code
• $ pip install scattertext
• We’re hiring
– talk to me or, if you can’t, go to CDKJobs.com
– http://www.jasonkessler.com to contact me
• Special thanks to the CDK Global Data Science team, including
Michael Mabale (thoughts on word clouds), Iris Laband, Peter
Kahn, Michael Eggerling, Kyle Lo, Iris Laband, Chris Mills, Phil
Burger, Dengyao Mo, and many others.
Acknowledgements

Questions?
• 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

F-Score-Percentile Fisher LogReg-L1 LogReg-L2 Rudder
1 fight for (108:7)
president obama
(351:78)
president obama
(351:78)
obama (537:165) auto (37:0)
2 auto (37:0) obama (537:165) barack (202:46)
president obama
(351:78)
insurance
company
(28:0)
3 fair (47:3)
president
(746:303)
cut (118:24) president (746:303)
america
forward (28:0)
4
insurance company
(28:0)
middle class
(149:18)
worker (54:9) for (1020:542)
auto industry
(24:0)
5
america forward
(28:0)
fight for (108:7) all of (61:23) barack (202:46) pell (23:0)
6 middle class (149:18) class (162:25) charlotte (28:3) forward (105:16) wealthy (23:0)
7
president barack
(47:4)
middle (164:27) today 's (12:3) fight (165:31)
pell grant
(23:0)
8 auto industry (24:0) barack (202:46) so that (33:9) fight for (108:7)
last week
(22:0)
9 pell grant (23:0) fight (165:31) forward (105:16) class (162:25)
the wealthy
(21:0)
10 pell (23:0) forward (105:16) that ' (236:91)
barack obama
(164:45)
romney say
(21:0)
11 wealthy (23:0) for (1020:542) make (299:200)
middle class
(149:18)
millionaire
(20:0)
The top words ranked by each method (dem:rep freqs)

This is a Rudder
diagram colored
with Rudder
scores.
The code is
available at
https://github.com
/JasonKessler/tex
t-to-ideas/

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