1. Automated Personality Classification
A. KARTELJ and V. FILIPOVIC
School of Mathematics, University of Belgrade, Serbia
and
V. MILUTINOVIC
School of Electrical Engineering, University of Belgrade, Serbia

2. Agenda Problem overview Classification of the existing solutions
Presentation of the existing solutions
Comparison of the solutions
Work in progress: Bayesian Structure Learning for the APC
Future work: Video Based APC
Conclusions
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3. Problem Overview
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4. The Big 5 Model
Openness to experience – (inventive/curious vs. consistent/cautious). Appreciation for art, emotion, adventure, unusual ideas, curiosity, and variety of experience. Openness reflects the degree of intellectual curiosity, creativity and a preference for novelty and variety. Some disagreement remains about how to interpret the openness factor, which is sometimes called "intellect" rather than openness to experience.
Conscientiousness – (efficient/organized vs. easy-going/careless). A tendency to show self-discipline, act dutifully, and aim for achievement; planned rather than spontaneous behavior; organized, and dependable.
Extraversion – (outgoing/energetic vs. solitary/reserved). Energy, positive emotions, surgency, assertiveness, sociability and the tendency to seek stimulation in the company of others, and talkativeness.
Agreeableness – (friendly/compassionate vs. cold/unkind). A tendency to be compassionate and cooperative rather than suspicious and antagonistic towards others.
Neuroticism – (sensitive/nervous vs. secure/confident). The tendency to experience unpleasant emotions easily, such as anger, anxiety, depression, or vulnerability. Neuroticism also refers to the degree of emotional stability and impulse control, and is sometimes referred by its low pole – "emotional stability".
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5. The Steps in Our Research
Survey paper (under review at ACM CSUR)
Research paper: A new APC model based on Bayesian structure learning (in progress)
Real-purpose application of the APC model from step 2
Go to step 3
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6. Elements of APC Corpus: Personality measurement: Model:
Essay, weblog, , news group, Twitter counts...
Personality measurement:
Questionnaire (internet and written). We are searching for an alternative!
Model:
Stylistic analysis, linguistic features, machine learning techniques
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7. Applications
Social networks – friend suggestions, dating sites (finding compatible partners)
Youtube, TripAdvisor, Google, eBay – personality based recommendations
Customer targeting, advertisement
Other usages – police, anti-terrorism etc.
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8. Mining People’s Characteristics
Authorship – who is an author of some non-signed piece of text?
Gender – is an author male or female?
Mood, emotions – emotions conveyed through text?
Opinion – mining opinion from text (positive, negative, …)?
Personality
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9. Classification of Solutions
C1 criterion separates solutions by type of conversation (1 = self-reflexive, N = continuous)
C2 criterion separates solutions by approach (TD = top-down, DD = data-driven, or HY = hybrid)
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10. Linguistic Styles: Language Use as an Individual Difference Pennebaker and King [1999]
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11. LIWC and MRC Features Feature Type Example Anger words LIWC Hate, kill
Metaphysical issues
God, heaven, coffin
Physical state / function
Ache, breast, sleep
Inclusive words
With, and, include
Social processes
Talk, us, friend
Family members
Mom, brother, cousin
Past tense verbs
Walked, were, had
References to friends
Pal, buddy, coworker
Imagery of words
MRC
Low: future, peace – High: table, car
Syllables per word
Low: a – High: uncompromisingly
Concreteness
Low: patience, candor – High: ship
Frequency of use
Low: duly, nudity – High: he, the
LIWC dictionary that represents a part of the
text analysis framework LIWC (Linguistic Inquiry and Word Count) developed by
Pennebaker et al. [2001]. LIWC categorizes words into meaningful psychological
categories.
Coltheart [1981] proposed the MRC, a psycholinguistic database of
words categorized by various linguistic features of text, such as: imagery, concrete-
ness, frequency of usage, etc.
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12. What Are They Blogging About
What Are They Blogging About? Personality, Topic and Motivation in Blogs Gill et al. [2009]
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13. Taking Care of the Linguistic Features of Extraversion Gill and Oberlander [2002]
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14. Personality Based Latent Friendship Mining Wang et al. [2009]
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15. A Comparative Evaluation of Personality Estimation Algorithms for the TWIN Recommender System Roshchina et al. [2011]
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16. Predicting Personality with Social Media Golbeck et al. [2011]
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17. Our Twitter Profiles, Our Selves: Predicting Personality with Twitter Quercia et al. [2011]
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18. M5’ rules, Gaussian processes 12 [Celli 2012] 1065 posts
Paper
Input
Corpus
Features
Algorithm
Soft.
Cit. I S A R
[Pennebaker and King 1999]
text
essays
LIWC
correlations
n/a
455 H M
[Mairesse et al. 2007]
text, speech
LIWC, MRC
C4.5, NB, SMO, M5’
Weka 99
[Gill et al. 2009]
weblogs (14.8words)
linear regression 26
[Yarkoni 2010]
weblogs (100K words) 21
[Gill and Oberlander 2002]
s (105 students)
bigrams
bigram analysis 49 L
[Nowson et al. 2005]
weblogs (410K words)
word list 48
[Oberlander 2006]
weblogs (410K words)
N-grams
NB, SMO 53
[Wang et al. 2009]
text,
weblogs (200 pairs)
lexical freq. ,TFIDF
logistic regression
Minitab 1
[Iacobelli et al. 2011]
weblogs (3000)
LIWC, bigrams,
SVM, SMO, NB..
[Argamon et al. 2005]
word list, conj.
SMO 38
[Argamon et al. 2007]
Weka, ATMan 45
[Mairesse and Walker 2006]
text , conv. extracts
96 persons
(≈100Kwords)
LIWC, MRC, utterance…
RankBoost 22
[Rigby and Hassan 2007]
mail. lists (140K s)
C4.5
Weka, SPSS 30
[Roshchina et al. 2011]
TripAdvisor reviews
LIWC, MRC
Linear, M5, SVM 2
[Quercia et al. 2011]
meta
335 Twitter users
Twitter counts
M5’ rules 5
[Golbeck et al. 2011]
text, meta
279 FB users
5 classes
(161 in total)
M5’ rules, Gaussian processes 12
[Celli 2012]
1065 posts
22 ling. Features
majority-based classification
I – implementation cost
S – scalability
A – availability
R – reliability
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19. Naive Bayes Classifier
Naive Bayes, Oberlander [2006]
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20. Naive Bayes and Bayesian Network
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21. Bayesian Network for the APC
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22. Bayesian Network Structure Learning
Obtain corpus (training set T)
Fit T to appropriate network structure by:
ILP formulation + solver (CPLEX, Gurobi…) on smaller instances
Apply metaheuristic on larger instances
Validate quality of metaheuristic approach
Compare obtained APC accuracy with other approaches
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23. Other Ideas Games with a purpose (GWAP)
Clustering personality characteristics
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24. Packing everything together: Video Based APC
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25. Conclusions Classification of the existing solutions (Survey paper)
Filling the gaps inside classification tree
Introducing Bayesian Structure Learning for the APC
Utilizing metaheuristics in dealing with high dimensionality
APC potential: social networks, recommender, and expert systems
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26. THANK YOU! Aleksandar Kartelj kartelj@matf.bg.ac.rs
Vladimir Filipovic
Veljko Milutinovic