Your writing reveals more about you than you think. Characteristics such as intelligence, political orientation, emotional stability, creativity, and job performance can reliably be predicted from even small amounts of text - sometimes even just from punctuation or pronouns. Natural Language Processing (NLP) uses statistical techniques to extract the underlying structural concepts from normal text. One of the most powerful uses of these techniques is to extract the personality characteristics of an author directly from their word choice. This talk will introduce some NLP techniques, show APIs (including IBM Watson) that allow us to extract personality dimensions, discuss the 5-factor (“Big 5”) Personality Model, and illuminate the predictive power that results. Finally, we will look at ways business are beginning to use this analysis and new possibilities - both positive and negative - now opening up due to availability of NLP personality mapping tools.

1. Natural Language Processing
(NLP) and Personality
Analysis

2. Who am I?
• David Rostcheck
(@davidrostcheck)
• I’m an architect and data
scientist
• Follow my videos
(YouTube) and articles
(LinkedIn)

3. super quick intro to NLP
(for those who
don’t know about it)

4. NLP: simple heuristic algorithms
that produce powerful results
by leveraging the deep
structure in the data

5. ex. topic extraction – the low-res LDA

6. language
encodes
sTruCTure

8. [Marco Polo goes to Asia and discovers
a world much more advanced than he
expects, with inventions (paper, paper
money, compass) that will revolutionize
Europe].

9. in the distant land of
Psychology, researchers built
out a sophisticated data-based
models of human behavior

10. they identified 2 major
elements:
intelligence
and
personality

11. each describes about 25% of the
variation between individuals
together they describe about 50%
they predict behavior and/or
performance in many areas

12. prediction areas:
- individual/job fit
- job performance
- wealth
- social success
- political orientation
- creativity
- emotional stability

13. this stuff is really useful, so
we rebranded
Psychology
to
Cognitive Science
and started taking it
seriously

14. As a data scientist,
how would you
characterize/model
personality?

15. psychometrics:
personality axes (traits) determined
by solving for response
eigenvectors through factor
analysis on large test sets
(personality tests)
purely data-driven approach

16. 5-factor (“Big 5”) model
characterizes personality along 5
independent major traits
- Openness
- Conscientiousness
- Extraversion
- Agreeableness
- Neuroticism

17. warning:
there are gender differences (in
statistical aggregate) in personality
traits
it’s a real thing
no shoot messenger/scientist

18. remember average
vs. the distribution

19. openness (to experience)
try new things!
break cate-go-ries
imaginative
artistic
strong predictor of political left

20. Conscientiousness:
-Orderly.
-Organized.
-Attention to detail.
-Dutiful.
-Self-disciplined.
-High predictor of job
performance
-High predictor of political
conservatism

21. extraversion!
willing to engage with the
world!
enthusiastic!
action-oriented!
assertive!
reversed: introversion

22. agreeableness
value harmony
cooperative
trusting
optimistic
considerate
kind
generous
women are significantly statistically more agreeable than
men

23. note: dis-agreeableness
associated with:
negotiation skill
executive leadership
economic success (pay raises)

24. trait “neuroticism”
(not the best word choice)
susceptibility to negative emotion
vulnerability to stress
anxiety
predictor of mental illness
increased attunement to social landscape
heavily loaded on estrogen (*)
(*) women average half a standard deviation
higher than men)

25. let’s have some fun and assess
personality traits!

26. openness to change: ??
conscientiousness: ??
extraversion: ??
agreeableness: ??
neuroticism (neg. emotion): ??

27. o: high med-low high
c: high high med-low
e: med-high med-low high
a: med-low low low
n: low low low

28. openness to change: ??
conscientiousness: ??
extraversion: ??
agreeableness: ??
neuroticism (neg. emotion): ??

29. o: low med-low high
c: low high low
e: med-high med-high med-low
a: low med-high low
n: med-high med-high med-low

30. traits subdivide into facets
ex: Openness (to experience)->
Intellect[ual curiosity]
(facet) Openness
There are 10-facet, 14-facet,
and 30-facet models

31. complexities:
- scores are percentile (relative to
others)
- poor name choices
- traits are coarse
- facet models differ (hard to compare)
- no standard facet naming

32. inferring
personality
from
text

33. demo: IBM Watson’s
Personality Insights
service

34. Does it work?

35. s-orta…

36. personality test
vs.
personal journal
300-question test w/ same facets as
Watson
88,125 word journal (plenty long)

37. 300q
test
Watson 88125
words
Trait percentile percentile
Agreeableness 25 9
Conscientiousness 72 58
Extraversion 69 31
Neuroticism 20 62
Openness (to
experience) 31 85

38. (high-level) traits do not
accurately match
personality test


39. what about the facets?

40. Big5
Extraversion(19%
)
Openness
(67%)
Adventurousness(78%)
Artisticinterests(78%)
Emotionality(50%)Imagination(51%)Intellect(99%
)
Authority-challenging
(95%
)
Conscientiousness
(58%)
Achievem
entstriving
(86%
)
Cautiousness (88%
)
Dutifulness (85%)
Orderliness (16%)
Self-discipline(75%)
Self-efficacy(76%)
Extraversion(19%)
Activitylevel(97%)
Assertiveness (85%)Cheerfulness (39%)
Excitement-seeking(31%)
Outgoing(26%
)
G regariousness
(8%
)
Agreeableness
(27%)
Altruism
(73%
)
Cooperation(71%
)
Modesty(46%)
Uncompromising(91%
Sympathy(92%)
Trust(73%)
Emotional range(54%)
Fiery(14%)
Pronetoworry(23%)
Melancholy(43%)
Immoderation(19%)
Self-consciousness(62%
)
Susceptible
to
stress
(47%
)
Values
Conservation
(11%)
Conservation
(11%
)
Openness tochange(86%
)H edonism (15%)
Self-enhancement(31%)Self-transcendence(69%) Nee
ds
Curi
osity
(91
%
)
Challenge(59%)
Closeness (17%)
Curiosity(91%)
Excitement(41%)
H armony(15%)
Ideal(74%
)
iberty
(51%
)
ove
(14%
)
Practicality(23%
)
Self-expression(32%)Stability(55%)
Structure(87%)

41. 300q test Watson 88125 words
Trait Facet Percentile Diff
Agreeableness
Sympathy 28 89 61
Altruism 35 56 21
Cooperation 46 45 1
Modesty 23 14 9
Morality (Uncomprimising) 37 35 2
Trust 29 65 36
Conscientiousness
Achievement Striving 74 75 1
Orderliness 67 21 46
Cautiousness 67 77 10
Dutifulness 51 66 15
Self-discipline 77 54 23
Self-efficacy 51 86 35
Extraversion
Activity level 84 88 4
Assertiveness 65 91 26
Cheerfullness 47 10 37
Excitement-seeking 46 30 16
Friendliness 66 30 36
Gregariousness 74 10 64
Neuroticism
Vulnerability 32 32 0
Anger/firey 38 34 4
Anxiety 22 32 10
Depression 14 64 50
Immoderation 26 24 2
Self-conscious 28 50 22
Openness (to experience)
Intellect[ual curiosity] 80 99 19
Authoritity-challenging (liberalism) 29 94 65
Imagination 19 47 28
Emotionality 22 17 5
Artistic interest 28 49 21
Adventurousness 51 68 17

42. facets concur reasonably well:
12 close
7 good
8 weak
5 far off

43. this is one example;
more general tests indicate
Watson can assess personality
at the facet level with
tolerable accuracy for many
applications, if given enough
text

44. see “The Science Behind the Service”

45. but how much text is
enough?

46. high and low values form a
strong fingerprint that is
preserved when the
document is cut down
severely
(here from 88,125 words to 1,418)

47. 300q test Watson 88125 words Watson 1418 words
Trait Facet Percentile
Agreeableness
Sympathy 28 89 92
Altruism 35 56 73
Cooperation 46 45 71
Modesty 23 14 46
Morality
(Uncompromising) 37 35 91
Trust 29 65 73
Conscientiousness
Achievement striving 74 75 86
Orderliness 67 21 16
Cautiousness 67 77 88
Dutifulness 51 66 85
Self-discipline 77 54 75
Self-efficacy 51 86 76
Extraversion
Activity level 84 88 97
Assertiveness 65 91 85
Cheerfullness 47 10 39
Excitement-seeking 46 30 31
Friendliness 66 30 26
Gregariousness 74 10 8
Neuroticism
Vulnerability 32 32 47
Angery/firey 38 34 14
Anxiety 22 32 23
Depression 14 64 43
Immoderation 26 24 19
Self-conscious 28 50 62
Openness (to experience)
Intellect[ual curiosity] 80 99 99
Authoritity-challenging
(liberalism) 29 94 95
Imagination 19 47 51
Emotionality 22 17 50
Artistic interest 28 49 78
Adventurousness 51 68 78
high/low
signals:
7 conf.
3 non

48. Watson has strong internal
consistency.
I have seen strong signals
preserved when cut down to as
few as 115 words.

49. personality fingerprint can
reliably identify an author
proposed application:
combating academic fraud

50. conclusions:

51. personality models
usefully describe
individuals

52. Watson can analyze personality
accurately enough for many
applications
(at the facet level, not the trait level)

53. facet highs & lows
seem to
form a stable “personality
fingerprint”

54. even small amounts
of text (~125 words) can
reproduce the “personality
fingerprint”
those highs/lows provide useful
signals for applications

55. epilog: for further research

56. Dr. Jordan Peterson
Personality researcher, see
“Maps of Meaning” book &
video lectures for detailed Big
5 explanation.
jordanbpeterson.com
His personality tests:
understandmyself.com

57. Dr. James Pennebaker
Personality analysis via
pronouns and metadata
Book and TEDx lecture at
secretlifeofpronouns.com
Research commercialized
through Receptiviti.

58. Questions?
Contact: davidrostcheck@lgmail.com twitter: @davidrostcheck
Articles: http://linkedin.com/in/davidrostcheck