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Automated Personality Classification

Explore the latest advancements in automated personality classification (APC), including Bayesian structure learning and video-based approaches. This paper compares existing solutions and outlines the potential applications of APC.

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Automated Personality Classification

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  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 MULTI 2012

  3. Problem Overview MULTI 2012

  4. The Big 5 Model MULTI 2012

  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 applicationof the APC model from step 2 • Go to step 3 MULTI 2012

  6. 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 2012

  7. Applications MULTI 2012

  8. Mining People’s Characteristics MULTI 2012

  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) MULTI 2012

  10. Linguistic Styles: Language Use as an Individual DifferencePennebakerand King [1999] MULTI 2012

  11. LIWC and MRC Features MULTI 2012

  12. What Are They Blogging About? Personality, Topic and Motivation in BlogsGill et al. [2009] MULTI 2012

  13. Taking Care of the Linguistic Features of ExtraversionGill and Oberlander [2002] MULTI 2012

  14. Personality Based Latent Friendship Mining Wang et al. [2009] MULTI 2012

  15. A Comparative Evaluation of Personality Estimation Algorithms for the TWIN Recommender System Roshchina et al. [2011] MULTI 2012

  16. Predicting Personality with Social MediaGolbecket al. [2011] MULTI 2012

  17. Our Twitter Profiles, Our Selves: Predicting Personality with TwitterQuercia et al. [2011] MULTI 2012

  18. MULTI 2012

  19. Naive Bayes Classifier MULTI 2012

  20. Naive Bayes and Bayesian Network MULTI 2012

  21. Bayesian Network for the APC MULTI 2012

  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 MULTI 2012

  23. Other Ideas Games with a purpose (GWAP) Clustering personality characteristics MULTI 2012

  24. Packing everything together: Video Based APC MULTI 2012

  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 MULTI 2012

  26. THANK YOU! AleksandarKarteljkartelj@matf.bg.ac.rs Vladimir Filipovicvladaf@matf.bg.ac.rs VeljkoMilutinovicvm@etf.bg.ac.rs

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