Automated Personality Automated Personality ClassificationClassification

A. KARTELJ and V. FILIPOVICSchool of Mathematics, University of Belgrade, SerbiaandV. MILUTINOVICSchool of Electrical Engineering, University of Belgrade, Serbia

AgendaAgendaProblem overviewClassification of the existing solutionsPresentation of the existing solutionsComparison of the solutionsWork in progress:

Bayesian Structure Learning for the APC

Future work: Video Based APC

ConclusionsMULTI 2012 23.10.2012

Problem Problem OOverviewverview

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The Big 5 ModelThe Big 5 Model

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The The SSteps in teps in OOur ur RResearchesearch1. Survey paper

(under review at ACM CSUR)2. Research paper:

A new APC model based on Bayesian structure learning (in progress)

3. Real-purpose applicationof the APC model from step 2

4. Go to step 3 MULTI 2012 53.10.2012

Elements of APCElements of APCCorpus:

Essay, weblog, email, 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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ApplicationsApplications

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Mining Mining People’s People’s CharacteristicsCharacteristics

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Classification of SolutionsClassification of Solutions

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• 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)

Linguistic Styles: Linguistic Styles: Language Use as an Individual Language Use as an Individual DifferenceDifferencePennebaker Pennebaker and King and King [[1999]1999]

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LIWC and MRC FeaturesLIWC and MRC FeaturesFeature Type ExampleAnger words LIWC Hate, killMetaphysical issues LIWC God, heaven, coffinPhysical state / function

LIWC Ache, breast, sleep

Inclusive words LIWC With, and, includeSocial processes LIWC Talk, us, friendFamily members LIWC Mom, brother, cousinPast tense verbs LIWC Walked, were, hadReferences to friends

LIWC Pal, buddy, coworker

Imagery of words MRC Low: future, peace – High: table, car

Syllables per word MRC Low: a – High: uncompromisingly

Concreteness MRC Low: patience, candor – High: ship

Frequency of use MRC Low: duly, nudity – High: he, the

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What Are They Blogging About? What Are They Blogging About? Personality, Topic and Motivation in Personality, Topic and Motivation in BlogsBlogsGill et al. [2009]Gill et al. [2009]

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Taking Care of the Linguistic Taking Care of the Linguistic Features Features of Extraversionof ExtraversionGill and OberGill and Oberlander [2002]lander [2002]

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Personality Based Latent Personality Based Latent Friendship Mining Friendship Mining Wang et al. [2009]Wang et al. [2009]

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A Comparative Evaluation of Personality Estimation A Comparative Evaluation of Personality Estimation Algorithms for the Algorithms for the TWIN Recommender System TWIN Recommender System Roshchina et al. [2011]Roshchina et al. [2011]

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Predicting Personality Predicting Personality with Social Mediawith Social MediaGolbeck et al. [2011]Golbeck et al. [2011]

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Our Twitter Profiles, Our Our Twitter Profiles, Our Selves: Predicting Selves: Predicting Personality with TwitterPersonality with TwitterQuercia et al. Quercia et al. [[20112011]]

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Paper Input Corpus Features Algorithm Soft. Cit. I S A R[Pennebaker and King 1999] text essays LIWC correlations n/a 455 H H H M

[Mairesse et al. 2007] text, speech essays LIWC, MRC C4.5, NB, SMO,

M5’ Weka 99 M M H M

[Gill et al. 2009] text weblogs (14.8words) LIWC linear regression n/a 26 H H M M

[Yarkoni 2010] text weblogs (100K words) LIWC correlations n/a 21 H M M M

[Gill and Oberlander 2002] text emails (105

students) bigrams bigram analysis n/a 49 L M M L

[Nowson et al. 2005] text weblogs (410K words) word list correlations n/a 48 L H H L

[Oberlander 2006] text weblogs (410K words) N-grams NB, SMO Weka 53 H M H M

[Wang et al. 2009] text, weblogs (200 pairs) lexical freq. ,TFIDF

logistic regression Minitab 1 H M M M

[Iacobelli et al. 2011] text weblogs (3000) LIWC, bigrams, SVM, SMO, NB.. Weka 1 H H M H

[Argamon et al. 2005] text essays word list, conj.SMO Weka 38 H M M M[Argamon et al. 2007] text essays word list, conj.SMO Weka,

ATMan 45 H M M M

[Mairesse and Walker 2006]

text , conv. extracts

96 persons (≈100Kwords)

LIWC, MRC, utterance… RankBoost n/a 22 M M H M

[Rigby and Hassan 2007] text mail. lists (140K

emails) LIWC C4.5 Weka, SPSS 30 M H M L

[Roshchina et al. 2011] text TripAdvisor

reviews LIWC, MRC Linear, M5, SVM Weka 2 H M L M[Quercia et al. 2011] meta 335 Twitter users Twitter counts M5’ rules Weka 5 M H M M

[Golbeck et al. 2011] text, meta 279 FB users 5 classes

(161 in total)M5’ rules, Gaussian processes

Weka 12 H M M M

[Celli 2012] text 1065 posts 22 ling. Features

majority-based classification n/a 1 M M M MMULTI 2012 183.10.2012

Naive Bayes ClassifierNaive Bayes Classifier

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Naive Bayes and Bayesian Naive Bayes and Bayesian NetworkNetwork

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Bayesian Network for the Bayesian Network for the APCAPC

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Bayesian Network Structure Bayesian Network Structure LearningLearning1. Obtain corpus (training set T)2. Fit T to appropriate network structure

by:a) ILP formulation + solver (CPLEX, Gurobi…)

on smaller instancesb) Apply metaheuristic on larger instances

3. Validate quality of metaheuristic approach

4. Compare obtained APC accuracy with other approaches

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Other IdeasOther Ideas

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Games with a purpose (GWAP)

Clustering personality characteristics

3.10.2012

Packing everything together: Packing everything together:

Video Based APCVideo Based APC

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ConclusionsConclusionsClassification of the existing solutions

(Survey paper)Filling the gaps inside classification

treeIntroducing Bayesian Structure

Learning for the APCUtilizing metaheuristics in dealing

with high dimensionalityAPC potential: social networks,

recommender, and expert systemsMULTI 2012 253.10.2012

THANK YOU.THANK YOU.Aleksandar Kartelj kartelj@matf.bg.ac.rsVladimir Filipovic vladaf@matf.bg.ac.rsVeljko Milutinovic vm@etf.bg.ac.rs