Computational Approaches to the Analysis of Human Creativity

  • Fabio Celli
Part of the Lecture Notes in Morphogenesis book series (LECTMORPH)


In this paper we address the issue of creativity and style computation from a natural language processing perspective. We introduce a computational framework for creativity analysis with two approaches, one agnostic, based on clustering, and one knowlegde-based, that exploits supervised learning and feature selection. While the agnostic approach can reveal the uniqueness of authors in a meaningful context, the knowledge-based approach can be exploited to extract the culturally relevant features of works and to predict social acceptance. In both the approaches, it is required a great effort to define symbols to represent meaningful cues in creativity and style.


  1. 1.
    Csikszentmihalyi, M.: Society, Culture, and Person: A Systems View of Creativity. Cambridge University Press, Cambridge (1988)Google Scholar
  2. 2.
    Csikszentmihalyi, M.: Flow and the Psychology of Discovery and Invention. HarperPerennial, New York (1997)Google Scholar
  3. 3.
    Kraft, U.: Unleashing creativity. Sci. Am. Mind 16(1), 16–23 (2005)CrossRefGoogle Scholar
  4. 4.
    Guilford, J.P.: Intelligence, Creativity, and Their Educational Implications. RR Knapp, San Diego (1968)Google Scholar
  5. 5.
    Torrance, E.P.: The nature of creativity as manifest in its testing. The Nature of Creativity pp. 43–75 (1988)Google Scholar
  6. 6.
    Piffer, D.: Can creativity be measured? an attempt to clarify the notion of creativity and general directions for future research. Think. Skills Creat. 7(3), 258–264 (2012). doi: 10.1016/j.tsc.2012.04.009
  7. 7.
    Kaufman, J.C., Lee, J., Baer, J., Lee, S.: Captions, consistency, creativity, and the consensual assessment technique: new evidence of reliability. Think. Skills Creat. 2(2), 96–106 (2007)CrossRefGoogle Scholar
  8. 8.
    Carson, S.H., Peterson, J.B., Higgins, D.M.: Reliability, validity, and factor structure of the creative achievement questionnaire. Creat. Res. J. 17(1), 37–50 (2005)CrossRefGoogle Scholar
  9. 9.
    Liu, Y.T.: Creativity or novelty?: cognitive-computational versus social-cultural. Des. Stud. 21(3), 261–276 (2000)CrossRefGoogle Scholar
  10. 10.
    Saunders, R., Gero, J.S.: Artificial creativity: A synthetic approach to the study of creative behaviour. Computational and Cognitive Models of Creative Design V, Key Centre of Design Computing and Cognition, pp. 113–139. University of Sydney, Sydney (2001)Google Scholar
  11. 11.
    Wiggins, G.: Categorising creative systems. In: Proceedings of Third (IJCAI) Workshop on Creative Systems: Approaches to Creativity in Artificial Intelligence and Cognitive Science. Citeseer (2003)Google Scholar
  12. 12.
    Barbieri, G., Pachet, F., Roy, P., Degli Esposti, M.: Markov constraints for generating lyrics with style. In: ECAI, pp. 115–120 (2012)Google Scholar
  13. 13.
    Runco, M.A., Nemiro, J., Walberg, H.J.: Personal explicit theories of creativity. J. Creat. Behav. 32(1), 1–17 (1998). doi: 10.1002/j.2162-6057.1998.tb00803.x
  14. 14.
    Boden, M.A.: Creativity: a framework for research. Behav. Brain Sci. 17(03), 558–570 (1994)CrossRefGoogle Scholar
  15. 15.
    Pribram, K.H.: Brain and the creative act. Encycl. Creat. 2, 213–217 (1999)Google Scholar
  16. 16.
    Mainemelis, C.: Stealing fire: creative deviance in the evolution of new ideas. Acad. Manag. Rev. 35(4), 558–578 (2010)CrossRefGoogle Scholar
  17. 17.
    Runco, M.A.: Creativity: Theories and Themes: Research, Development, and Practice. Academic Press (2010)Google Scholar
  18. 18.
    Averill, J.R.: Creativity in the domain of emotion. Handbook of Cognition and Emotion, pp. 765–782 (1999)Google Scholar
  19. 19.
    Grira, N., Crucianu, M., Boujemaa, N.: Unsupervised and semi-supervised clustering: a brief survey. A Review of Machine Learning Techniques for Processing Multimedia Content, p. 11 (2004)Google Scholar
  20. 20.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  21. 21.
    Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica (03505596) 31(3) (2007)Google Scholar
  22. 22.
    Molina, L.C., Belanche, L., Nebot, À.: Feature selection algorithms: a survey and experimental evaluation. In: IEEE International Conference on Data Mining, pp. 306–313. IEEE (2002)Google Scholar
  23. 23.
    Salappa, A., Doumpos, M., Zopounidis, C.: Feature selection algorithms in classification problems: an experimental evaluation. Optim. Methods Softw. 22(1), 199–212 (2007)CrossRefGoogle Scholar
  24. 24.
    Mihalcea, R., Strapparava, C.: Lyrics, music, and emotions. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 590–599. Association for Computational Linguistics (2012)Google Scholar
  25. 25.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington, MA (2011)Google Scholar
  26. 26.
    Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the smo algorithm for svm regression. IEEE Trans. Neural Netw. 11(5), 1188–1193 (2000)CrossRefGoogle Scholar
  27. 27.
    Hall, M.A., Smith, L.A.: Practical Feature Subset Selection for Machine Learning. Springer, Berlin(1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of TrentoTrentoItaly

Personalised recommendations