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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

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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 MULTI 201223.10.2012

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Problem Overview MULTI 201233.10.2012

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The Big 5 Model MULTI 201243.10.2012

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The Steps in Our Research 1. Survey paper (under review at ACM CSUR) 2. Research paper: A new APC model based on Bayesian structure learning (in progress) 3. Real-purpose application of the APC model from step 2 4. Go to step 3 MULTI 201253.10.2012

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Elements of APC Corpus: 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 MULTI 201263.10.2012

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Applications MULTI 201273.10.2012

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Mining Peoples Characteristics MULTI 201283.10.2012

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Classification of Solutions MULTI 201293.10.2012 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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Linguistic Styles: Language Use as an Individual Difference Pennebaker and King [1999] MULTI 2012103.10.2012

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LIWC and MRC Features FeatureTypeExample Anger wordsLIWCHate, kill Metaphysical issuesLIWCGod, heaven, coffin Physical state / functionLIWCAche, breast, sleep Inclusive wordsLIWCWith, and, include Social processesLIWCTalk, us, friend Family membersLIWCMom, brother, cousin Past tense verbsLIWCWalked, were, had References to friendsLIWCPal, buddy, coworker Imagery of wordsMRCLow: future, peace High: table, car Syllables per wordMRCLow: a High: uncompromisingly ConcretenessMRCLow: patience, candor High: ship Frequency of useMRCLow: duly, nudity High: he, the MULTI 2012113.10.2012

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What Are They Blogging About? Personality, Topic and Motivation in Blogs Gill et al. [2009] MULTI 2012123.10.2012

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Taking Care of the Linguistic Features of Extraversion Gill and Oberlander [2002] MULTI 2012133.10.2012

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Personality Based Latent Friendship Mining Wang et al. [2009] MULTI 2012143.10.2012

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A Comparative Evaluation of Personality Estimation Algorithms for the TWIN Recommender System Roshchina et al. [2011] MULTI 2012153.10.2012

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Predicting Personality with Social Media Golbeck et al. [2011] MULTI 2012163.10.2012

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Our Twitter Profiles, Our Selves: Predicting Personality with Twitter Quercia et al. [2011] MULTI 2012173.10.2012

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PaperInputCorpusFeaturesAlgorithmSoft.Cit.ISAR [Pennebaker and King 1999]textessaysLIWCcorrelationsn/a455HHHM [Mairesse et al. 2007]text, speechessaysLIWC, MRCC4.5, NB, SMO, M5Weka99MMHM [Gill et al. 2009]textweblogs (14.8words)LIWClinear regressionn/a26HHMM [Yarkoni 2010]textweblogs (100K words)LIWCcorrelationsn/a21HMMM [Gill and Oberlander 2002]textemails (105 students)bigramsbigram analysisn/a49LMML [Nowson et al. 2005]textweblogs (410K words)word listcorrelationsn/a48LHHL [Oberlander 2006]textweblogs (410K words)N-gramsNB, SMOWeka53HMHM [Wang et al. 2009]text,weblogs (200 pairs)lexical freq., TFIDF logistic regressionMinitab1HMMM [Iacobelli et al. 2011]textweblogs (3000)LIWC, bigrams,SVM, SMO, NB..Weka1HHMH [Argamon et al. 2005]textessaysword list, conj.SMOWeka38HMMM [Argamon et al. 2007]textessaysword list, conj.SMO Weka, ATMan 45HMMM [Mairesse and Walker 2006] text, conv. extracts 96 persons ( 100Kwords) LIWC, MRC, utterance RankBoostn/a22MMHM [Rigby and Hassan 2007]textmail. lists (140K emails)LIWCC4.5Weka, SPSS30MHML [Roshchina et al. 2011]textTripAdvisor reviewsLIWC, MRCLinear, M5, SVMWeka2HMLM [Quercia et al. 2011]meta335 Twitter usersTwitter countsM5 rulesWeka5MHMM [Golbeck et al. 2011]text, meta279 FB users 5 classes (161 in total) M5 rules, Gaussian processes Weka12HMMM [Celli 2012]text1065 posts22 ling. Features majority-based classification n/a1MMMM MULTI 2012183.10.2012

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Naive Bayes Classifier MULTI 2012193.10.2012

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Naive Bayes and Bayesian Network MULTI 2012203.10.2012

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Bayesian Network for the APC MULTI 2012213.10.2012

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Bayesian Network Structure Learning 1. Obtain corpus (training set T) 2. Fit T to appropriate network structure by: a)ILP formulation + solver (CPLEX, Gurobi) on smaller instances b)Apply metaheuristic on larger instances 3. Validate quality of metaheuristic approach 4. Compare obtained APC accuracy with other approaches MULTI 2012223.10.2012

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Other Ideas MULTI 201223 Games with a purpose (GWAP) Clustering personality characteristics 3.10.2012

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Packing everything together: Video Based APC MULTI 2012243.10.2012

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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 MULTI 2012253.10.2012

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THANK YOU! Aleksandar Kartelj kartelj@matf.bg.ac.rskartelj@matf.bg.ac.rs Vladimir Filipovic vladaf@matf.bg.ac.rsvladaf@matf.bg.ac.rs Veljko Milutinovic vm@etf.bg.ac.rsvm@etf.bg.ac.rs