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Big Five Personality Recognition from Multiple Text Genres

  • Vitor Garcia dos Santos
  • Ivandré ParaboniEmail author
  • Barbara Barbosa Claudino Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)

Abstract

This paper investigates which Big Five personality traits are best predicted by different text genres, and how much text is actually needed for the task. To this end, we compare the use of ‘free’ Facebook text with controlled text elicited from visual stimuli in descriptive and referential tasks. Preliminary results suggest that certain text genres may be more revealing of personality traits than others, and that some traits are recognisable even from short pieces of text. These insights may aid the future design of more accurate models of personality based on highly focused tasks for both language production and interpretation.

Keywords

Big Five Personality recognition 

Notes

Acknowledgements

This work has been supported by FAPESP grant 2016/14223-0.

References

  1. 1.
    Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: CLEF 2015 Evaluation Labs and Workshop (2015)Google Scholar
  2. 2.
    Argamon, S., Dhawle, S., Koppel, M., Pennebaker, J.W.: Lexical predictors of personality type. In: The Joint Annual Meeting of the 37th Interface Symposium and the CSNA (2005)Google Scholar
  3. 3.
    Mairesse, F.: Learning to adapt in dialogue systems: data-driven models for personality recognition and generation. Ph.D. thesis, University of Sheffield (2008)Google Scholar
  4. 4.
    Oberlander, J., Nowson, S.: Whose thumb is it anyway? Classifying author personality from weblog text. In: COLING/ACL 2006 Poster Sessions, Sydney, Australia, pp. 627–634 (2006)Google Scholar
  5. 5.
    Gill, A.J., Nowson, S., Oberlander, J.: What are they blogging about? Personality, topic and motivation in blogs. In: ICWSM-2009, pp. 18–25. The AAAI Press (2009)Google Scholar
  6. 6.
    Nowson, S., Oberlander, J.: Identifying more bloggers: towards large scale personality classification of personal weblogs. In: International Conference on Weblogs and Social Media (2007)Google Scholar
  7. 7.
    Celli, F.: Adaptive personality recognition from text. Ph.D. thesis, University of Trento (2012)Google Scholar
  8. 8.
    Farnadi, G., Zoghbi, S., Moens, M.F., de Cock, M.: Recognising personality traits using Facebook status updates. In: Workshop on Computational Personality Recognition (2013)Google Scholar
  9. 9.
    Plank, B., Hovy, D.: Personality traits on Twitter - or - how to get 1,500 personality tests in a week. In: Proceedings of WASSA-2015, pp. 92–98 (2015)Google Scholar
  10. 10.
    Iacobelli, F., Gill, A.J., Nowson, S., Oberlander, J.: Large scale personality classification of bloggers. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6975, pp. 568–577. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24571-8_71 CrossRefGoogle Scholar
  11. 11.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Inquiry and Word Count: LIWC. Lawrence Erlbaum, Mahwah (2001)Google Scholar
  12. 12.
    Álvarez-Carmona, M., López-Monroy, A., Montes-y-Gómez, M., Villaseñor-Pineda, L., Escalante, H.: INAOE’s participation at PAN 2015: author profiling task. In: CLEF 2015 (2015)Google Scholar
  13. 13.
    González-Gallardo, C., et al.: Tweets classification using corpus dependent tags, character and POS N-grams. In: CLEF 2015 (2015)Google Scholar
  14. 14.
    Ṣulea, O.M., Dichiu, D.: Automatic profiling of Twitter users based on their tweets. In: CLEF 2015 (2015)Google Scholar
  15. 15.
    John, O.P., Donahue, E.M., Kentle, R.L.: The big five inventory - versions 4a and 54. University of California, Berkeley, CA, Technical report (1991)Google Scholar
  16. 16.
    Dan-Glauser, E.S., Scherer, K.R.: The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance. Behav. Res. Methods 43(2), 468–477 (2011)CrossRefGoogle Scholar
  17. 17.
    Teixeira, C.V.M., Paraboni, I., da Silva, A.S.R., Yamasaki, A.K.: Generating relational descriptions involving mutual disambiguation. In: Gelbukh, A. (ed.) CICLing 2014. LNCS, vol. 8403, pp. 492–502. Springer, Heidelberg (2014). doi: 10.1007/978-3-642-54906-9_40 CrossRefGoogle Scholar
  18. 18.
    Paraboni, I., Galindo, M., Iacovelli, D.: Stars2: a corpus of object descriptions in a visual domain. Lang. Resour. Eval. 51(2), 49–62 (2016)Google Scholar
  19. 19.
    Ferreira, T.C., Paraboni, I.: Generating natural language descriptions using speaker-dependent information. Nat. Lang. Eng. 1–22 (2017). doi: 10.1017/S1351324917000079
  20. 20.
    Righi, G., Peissig, J.J., Tarr, M.J.: Recognizing disguised faces. Vis. Cogn. 20(2), 143–169 (2012)CrossRefGoogle Scholar
  21. 21.
    de Lucena, D.J., Paraboni, I., Pereira, D.B.: From semantic properties to surface text: the generation of domain object descriptions. Inteligencia Artificial. Revista Iberoamericana de. Inteligencia Artificial 14(45), 48–58 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vitor Garcia dos Santos
    • 1
  • Ivandré Paraboni
    • 1
    Email author
  • Barbara Barbosa Claudino Silva
    • 1
  1. 1.School of Arts, Sciences and HumanitiesUniversity of São PauloSão PauloBrazil

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