Past work on lexicon mining and discussion of a new python library for visualizing differences in language.

1. 1
Jason S. Kessler | Data Day Texas, January 14, 2017
@jasonkessler
Scattertext: A Tool for
Visualizing Differences in
Language

2. 2
Word frequency
• Women and men tend to use different terms on Facebook.
• As do introverts and extroverts.
• Hillary Clinton and Donald Trump used different terms in the
presidential debate.
• Reveal differences in
• content,
• perceived strengths and weaknesses
• communication style
• These are often obvious after being surfaced

3. 3
Outline
• Previous work
• Ways of visualizing word association
• Scattertext
• Open-source Python/D3 framework for visualizing these
differences
• Inspecting LDA, word2vec, sparse classification models
• How CDK Global is using this to help dealerships better sell
cars.
• We’re hiring senior data scientists + devs in Austin and Seattle.

4. 4
OKCupid: an online dating site
hobos
almond
butter
100 Years of
Solitude
Bikram yoga
Christian Rudder: http://blog.okcupid.com/index.php/page/7/
Which words
and phrases
statistically
distinguish
ethnic groups
and genders?

5. 5Source: Christian Rudder.
Dataclysm. 2014.
Ranking with everyone else
High distance: white men
ignore k-pop
Low distance: white men
disproportionately mention
Phish
The smaller the
distance from the top
left, the higher the
association with
white men.

6. 6Source: Christian Rudder.
Dataclysm. 2014.
my blue eyes

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Scattertext: Democrats vs Republicans: 2012 Convention Speeches

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Word Use Reflecting Gender and Personality in Facebook
Statuses
• Objective:
• Find words, phrases, and topics that correlate to
• gender, and
• Big 5 personality type
• Data source:
• My Personality App
• 75k voluntary participants in Facebook based survey, >300mm
words
• Agreed to give researchers access to statuses.
• Scoring algorithm
• Linear regression weights, 2000 LDA topics. Lyle Ungar
2013 AAAI
TutorialSchwartz et al. Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary
Approach. Plos One. 2013.

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Lyle Ungar
2013 AAAI
Tutorial
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 and topics

10. 10
Lyle Ungar
2013 AAAI
Tutorial
The bad:
• “Mullets of the
Internet” --Jeffrey
Zeldman, 2005
• Longer phrases are
are more prominent.
• Ranking is unclear
• Does size indicate
higher frequency?

11. 11
Lyle Ungar
2013 AAAI
Tutorial

12. 12
Lyle Ungar
2013 AAAI
Tutorial

13. 13
Mike Bostock et al., http://www.nytimes.com/interactive/2012/09/06/us/politics/convention-word-counts.html
NYT: 2012 Political Convention Word Use by Party

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

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Monroe, Burt L., Michael P. Colaresi, and
Kevin M. Quinn. "Fightin'words: Lexical
feature selection and evaluation for identifying
the content of political conflict." Political
Analysis 16.4 (2008): 372-403.
Differenceinz-scoresoflog-oddsw/prior
log
#(𝑤, 𝐴)
𝐴 − #(𝑤, 𝐴)
− 𝑙𝑜𝑔
#(𝑤, 𝐵)
𝐵 − #(𝑤, 𝐵)
Log-odds for word w, categories A,B
log
# 𝑤, 𝐴 + #(𝑤, 𝐶)
𝐴 + |𝐶| − #(𝑤, 𝐴) − #(𝑤, 𝐶)
− ⋯
Log-odds w/ Dirichlet prior, given
background corpus C
• Difference in z-score accounts for
variation in word frequencies.
• Words with differences < 1.96 are greyed
out.

16. 16
Monroe, Burt L., Michael P. Colaresi, and
Kevin M. Quinn. "Fightin'words: Lexical
feature selection and evaluation for identifying
the content of political conflict." Political
Analysis 16.4 (2008): 372-403.
Differenceinz-scoresoflog-oddsw/prior
• Pros:
• Popular among major CL
researchers (3rd edition of J+M)
• Favors words which appear less
frequent in background.
• Natural linear word listing
• Cons:
• You have to pick a
representative, large background
corpus.
• If the corpus is small, divide
by 0 issue
• Probably only practical for
unigrams
• Inefficient use of space on chart

17. 17Page 17
Repo: https://github.com/JasonKessler/scattertext
$ pip install scattertext
Why the plots look the way they do:
http://bit.ly/scattertextdevelopment
Topic models, word vectors, and The Lasso:
http://bit.ly/scattertext2016debates
Movie revenue and practical use:
http://bit.ly/scattertextrevenuemovie
Hands-on Tutorial

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CDK Global: Finding Words that Sell Cars
…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:
# who went on to visit
a Chevy dealer’s
website: 15
20
Conversion rate of everyone who read
review:
15/20=75%
Text:
Car Reviewed: Chevy Cruze
Median conversion
rate: 22%

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CDK Global: Finding Words that Sell Cars
5 star review words
Love
Comfortable
Features
Solid
Amazing
<3 star review words
Transmission
Problem
Issue
Dealership
Times

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CDK Global: Finding Words that Sell Cars
5 star review words High conversion words
Love Comfortable
Comfortable Front [Seats]
Features Acceleration
Solid Free [Car Wash, Oil Change]
Amazing Quiet
<3 star review words
Transmission
Problem
Issue
Dealership
Times

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CDK Global: Finding Words that Sell Cars
5 star review words High conversion words
Love Comfortable
Comfortable Front [Seats]
Features Acceleration
Solid Free [Car Wash, Oil Change]
Amazing Quiet
<3 star review words Low conversion words
Transmission Money [spend my, save]
Problem Features
Issue Dealership
Dealership Amazing
Times Build Quality [typically positive]

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CDK Global: Finding Words that Sell Cars (SUV Specific)
5 star review words High conversion words
Love Comfortable
Comfortable Front [Seats]
Features Acceleration
Solid Free [Car Wash, Oil Change]
Amazing Quiet
<3 star review words Low conversion words
Transmission Money [spend my, save]
Problem Features
Issue Dealership
Dealership Amazing
Times Build Quality [typically positive]
The worst thing you can say about an
SUV may be:
I saved money and got all these
amazing features!

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Thank you.
[first].[last]@gmail.com .
Please see https://github.com/JasonKessler/scattertext
for more info on this project.
We are hiring data scientists and developers in Seattle and
Austin! Please contact me if you’d like to know more.
https://jobs.cdkglobal.com/