Model for Personality Detection Based on Text Analysis

  • Yasmín HernándezEmail author
  • Carlos Acevedo Peña
  • Alicia Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11289)


Personality is a unique trait which distinguish people from each other. It is a set of individual differences in thinking, feeling and behaving of people, and it affects interaction, relationships and environment of people. Personality can be useful to several tasks like education, training, marketing and personnel recruitment. Several methods to detect personality have been proposed and there are several psychological models proposing different personality dimensions. Previous research states that personality can be detected by means of text analysis. We have built a model for personality detection based on statistical analysis of language and DISC model. As fundamental components of the model, we built a linguistic corpus with personality annotations and a corpus of words related to personality. To build the model, we conducted a study where 120 individuals participated. The study consisted in filling a personality test and writing some paragraphs. We trained several machine learning algorithms with data from the study, and we found Sequential Minimal Optimization algorithm achieved best results in classification.


DISC model Personality linguistic corpus Machine learning Personality detection Text analysis 



This research has been partially funded by European Commission and CONACYT, through the SmartSDK project.


  1. 1.
    Kazdin, A.E. (ed.): Encyclopedia of Psychology: 8 Volume Set. American Psychological Association and Oxford University Press (2000).
  2. 2.
    Pratama, B.Y., Sarno, R.: Personality classification based on Twitter text using Naïve Bayes, KNN and SVM. In: 2015 International Conference on Data Software Engineering, pp. 170–174 (2015)Google Scholar
  3. 3.
    Cantador, I., Fernández-Tobías, I., Bellogín, A.: Relating personality types with user preferences in multiple entertainment domains. In: CEUR Workshop Proceedings, vol. 997 (2013)Google Scholar
  4. 4.
    Golbeck, J., Robles, C., Edmondson, M., Turner, K.: Predicting personality from Twitter. In: Proceedings of the 2011 IEEE International Conference on Privacy, Security Risk Trust IEEE International Conference on Socity Computing PASSAT/SocialCom 2011, pp. 149–156 (2011)Google Scholar
  5. 5.
    Tupes, E.C., Christal, R.E.: Recurrent personality factors based on trait ratings. J. Pers. 60(2), 225–251 (1992)CrossRefGoogle Scholar
  6. 6.
    Eysenck, H.J.: Dimensions of Personality. Transaction Publishers, Piscataway (1950)Google Scholar
  7. 7.
    Marston, W.M.: Emotions of Normal People. Harcourt, Brace and Company, New York (1928)CrossRefGoogle Scholar
  8. 8.
    A&S Ltd.: What is DISC? Accessed 01 Jan 2017
  9. 9.
    Achaerandio, Y.S., Navarro, C., Sáez, A.M., Viñuela, P.I.: A system for personality and happiness detection. Ijimai 2(5), 8–16 (2014)Google Scholar
  10. 10.
    Lima, A.C.E.S., de Castro, L.N.: A multi-label, semi-supervised classification approach applied to personality prediction in social media. Neural Netw. 58, 122–130 (2014)CrossRefGoogle Scholar
  11. 11.
    Wald, R., Khoshgoftaar, T., Sumner, C.: Machine prediction of personality from Facebook profiles. In: Proceedings of the 2012 IEEE 13th International Conference on Information Reuse and Integration, IRI 2012, pp. 109–115 (2012)Google Scholar
  12. 12.
    Yuniar, I., Agung, A.A.G.: Personality Assessment Website using DISC. In: 2016 International Conference on Information Management and Technology, no. November, pp. 72–77 (2016)Google Scholar
  13. 13.
    DISC profile: Perfil en el lugar de trabajo [PDF file] (2013). Accessed 05 Jan 2017
  14. 14.
    D. Insight: The DISC Insights Web Development Team. Accessed 05 Jan 2017
  15. 15.
    D. P. 4U: DISC Behavioral Styles. Accessed 05 Jan 2017
  16. 16.
    Adali, S., Golbeck, J.: Predicting personality with social behavior. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 302–309 (2012)Google Scholar
  17. 17.
    Annalyn, N., Bos, M.W., Sigal, L., Li, B.: Predicting personality from book preferences with user-generated content labels. IEEE Trans. Affect. Comput. 3045(c), 1–12 (2018)CrossRefGoogle Scholar
  18. 18.
    Pennebaker, J., King, L.: Linguistic styles: language use as an individual difference. J. Personal. Soc. 77(6), 1296–1312 (1999)CrossRefGoogle Scholar
  19. 19.
    Pennebaker, J.W., Booth, R.J.: LIWC. Accessed 10 Jan 2017
  20. 20.
    Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)CrossRefGoogle Scholar
  21. 21.
    Luyckx, K., Daelemans, W.: Personae: a corpus for author and personality prediction from text. In: Sixth International Conference Language Resources and Evaluatio, LR 2008, no. May 2017, pp. 2981–2987 (2008). A European Language ResourcesGoogle Scholar
  22. 22.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 2nd edn. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  23. 23.
    Loyola, O., Medina, M.A., García, M.: Inducing decision trees based on a cluster quality index. IEEE Latin Am. Trans. 13(4), 1141–1147 (2015)CrossRefGoogle Scholar
  24. 24.
    Witten, I.H., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques. 2nd edn. Morgan Kaufmann Publishers, Burlington (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yasmín Hernández
    • 1
    Email author
  • Carlos Acevedo Peña
    • 2
  • Alicia Martínez
    • 2
  1. 1.Instituto Nacional de Electricidad y Energías Limpias, Gerencia de Tecnologías de la InformaciónCuernavacaMexico
  2. 2.Tecnológico Nacional de México, CENIDETCuernavacaMexico

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