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Personality Recognition Using Convolutional Neural Networks

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10762))

Abstract

Personality Recognition is an emerging task in Natural Language Processing due to its potential applications. However, the models which address this task rely on handcrafted resources; therefore, they are restricted by the domain of the problem and by the availability of resources. We propose a Convolutional Neural Network architecture trained using pre-trained word embeddings that is capable of learning the best features for the task at hand without any external dependence. The results show the potential of this approximation. The proposed model achieves comparable results with state-of-the-art models and is able to predict the personality traits of authors regardless of the social network and the availability of resources.

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Notes

  1. 1.

    http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/personality-insights.html.

  2. 2.

    These word vector representations are available at the following URL: http://nlp.stanford.edu/projects/glove/.

  3. 3.

    For further information about the gathering and labeling process see [25].

  4. 4.

    http://nlp.stanford.edu/software/tokenizer.shtml.

References

  1. Alekseev, A., Nikolenko, S.: Predicting the age of social network users from user-generated texts with word embeddings. In: Proceeding of the AINL FRUCT 2016 Conference, pp. 3–13 (2016)

    Google Scholar 

  2. Álvarez-Carmona, M.A., López-Monroy, A.P., Montes-y Gómez, M., Villaseñor-Pineda, L., Escalante, H.J.: Inaoes participation at pan: author profiling task. Working Notes Papers of the CLEF, pp. 1–10 (2015)

    Google Scholar 

  3. Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, genre, and writing style in formal written texts. TEXT 23(3), 321–346 (2003)

    Article  Google Scholar 

  4. Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., Stillwell, D.: Personality and patterns of facebook usage. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 24–32. ACM (2012)

    Google Scholar 

  5. Bengio, Y., Schwenk, H., Senécal, J.S., Morin, F., Gauvain, J.L.: Neural probabilistic language models. In: Holmes, D.E., Jain, L.C. (eds.) Innovations in Machine Learning. Studies in Fuzziness and Soft Computing, vol. 194. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  6. Boyle, G.J., Matthews, G., Saklofske, D.H.: The SAGE Handbook of Personality Theory and Assessment: Personality Measurement and Testing, vol. 2. Sage, Los Angeles (2008)

    Google Scholar 

  7. Cantador, I., Fernández-Tobías, I., Bellogín, A.: Relating personality types with user preferences in multiple entertainment domains. In: CEUR Workshop Proceedings. Shlomo Berkovsky (2013)

    Google Scholar 

  8. Celli, F., Rossi, L.: The role of emotional stability in twitter conversations. In: Proceedings of the Workshop on Semantic Analysis in Social Media, pp. 10–17. Association for Computational Linguistics (2012)

    Google Scholar 

  9. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

    MATH  Google Scholar 

  10. Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., Davalos, S., Moens, M.F., De Cock, M.: Computational personality recognition in social media. User Model. User Adapt. Interact. 26(2), 109–142 (2016). https://doi.org/10.1007/s11257-016-9171-0

    Article  Google Scholar 

  11. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org

  12. Kim, Y.: Convolutional neural networks for sentence classification, pp. 1–6 (2014). arXiv:1408.5882

  13. Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., Graepel, T.: Manifestations of user personality in website choice and behaviour on online social networks. Mach. Learn. 95(3), 357–380 (2014)

    Article  MathSciNet  Google Scholar 

  14. Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102(12), 1934–1939 (2014)

    Article  Google Scholar 

  15. LeCun, Y.: Generalization and network design strategies. In: Pfeifer, R., Schreter, Z., Fogelman, F., Steels, L. (eds.) Connectionism in Perspective. Elsevier, Zurich, Switzerland (1989)

    Google Scholar 

  16. Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30, 457–500 (2007)

    Article  Google Scholar 

  17. Mirkin, S., Nowson, S., Brun, C., Perez, J.: Motivating personality-aware machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1102–1108. Association for Computational Linguistics (2015)

    Google Scholar 

  18. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)

    Google Scholar 

  19. Oberlander, J., Nowson, S.: Whose thumb is it anyway?: classifying author personality from weblog text. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 627–634. Association for Computational Linguistics (2006)

    Google Scholar 

  20. Paruma-Pabón, O.H., González, F.A., Aponte, J., Camargo, J.E., Restrepo-Calle, F.: Finding relationships between socio-technical aspects and personality traits by mining developer e-mails. In: Proceedings of the 9th International Workshop on Cooperative and Human Aspects of Software Engineering, pp. 8–14. ACM (2016)

    Google Scholar 

  21. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. http://www.aclweb.org/anthology/D14-1162 (2014)

  22. Plank, B., Hovy, D.: Personality traits on twitter or how to get 1,500 personality tests in a week. In: Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 92–98 (2015)

    Google Scholar 

  23. Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our twitter profiles, our selves: predicting personality with twitter. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), pp. 180–185. IEEE (2011)

    Google Scholar 

  24. Rammstedt, B., John, O.P.: Measuring personality in one minute or less: a 10-item short version of the big five inventory in english and german. J. Res. Personal. 41(1), 203–212 (2007)

    Article  Google Scholar 

  25. Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at pan 2015. In: Proceedings of the Conference and Labs of the Evaluation Forum (Working Notes), pp. 518–538 (2015)

    Google Scholar 

  26. Schwartz, H.A.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PloS One 8(9), 1–16 (2013)

    Article  Google Scholar 

  27. Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the companion publication of the 23rd International Conference on World Wide Web, pp. 373–374. International World Wide Web Conferences Steering Committee (2014)

    Google Scholar 

  28. Verhoeven, B., Daelemans, W., Plank, B.: Twisty: a multilingual twitter stylometry corpus for gender and personality profiling. In: 10th International Conference on Language Resources and Evaluation (LREC 2016) (2016)

    Google Scholar 

  29. Youyou, W., Kosinski, M., Stillwell, D.: Computer-based personality judgments are more accurate than those made by humans. Proc. Natl Acad. Sci. 112(4), 1036–1040 (2015)

    Article  Google Scholar 

  30. Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification (2015). arXiv:1510.03820

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Acknowledgements

This work was developed in the framework of the TIN2015-71147-C2-1-P research project SOcial Media language understanding-EMBEDing contexts (SomEMBED), funded by the Ministry of Economy and Sustainability (MINECO). The work of the first author is financed by Grant PAID-01-2461 2015, from the Universitat Politècnica de València.

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Authors and Affiliations

  1. Pattern Recognition and Human Language Technology (PRHLT) Research Center, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    Maite Giménez, Roberto Paredes & Paolo Rosso

Authors
  1. Maite Giménez
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  2. Roberto Paredes
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  3. Paolo Rosso
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Correspondence to Maite Giménez .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Giménez, M., Paredes, R., Rosso, P. (2018). Personality Recognition Using Convolutional Neural Networks. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_23

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  • DOI: https://doi.org/10.1007/978-3-319-77116-8_23

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