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Canadian Conference on Artificial Intelligence

Canadian AI 2017: Advances in Artificial Intelligence pp 278–283Cite as

Using Cognitive Computing to Get Insights on Personality Traits from Twitter Messages

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10233))

Abstract

In this work, we apply text mining algorithms on Twitter messages made available by PAN2015. In the first step, we applied IBM Watson algorithms to obtain the results of Big Five personality analysis, namely OCEAN: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism. Then, we applied a Deep Learning algorithm on the resulted IBM Watson scores, in order to minimize the Root Mean Square Error. In this way, we achieved better results than using the IBM Watson algorithms alone. The dataset contains messages in English from 152 distinct authors.

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Notes

  1. 1.

    In short, to collect the ground truth data, when IBM was developing the tool, surveys were administered over large populations and, for each user, standard psychometric surveys were collected along with their Twitter posts.

References

  1. Candel, A., Parmar, V., LeDell, E., Arora, A.: Deep Learning with H2O. H2O.ai, Inc., Mountain View (2015)

    Google Scholar 

  2. Celli, F.: Unsupervised personality recognition for social network sites. In: Proceedings of 6th International Conference on Digital Society. Citeseer (2012)

    Google Scholar 

  3. Álvarez-Carmona, M.A., et al.: INAOE’s participation at PAN’15: author profiling task–notebook for PAN at CLEF 2015. In: Cappellato, L., Ferro, N., Jones, G., Juan, E.S. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, Toulouse, France. CEUR-WS.org, 8–11 September 2015

    Google Scholar 

  4. Farnadi, G., Sitaraman, G., Sushmita, S.: User modeling and user-adapted interaction, vol. 26 (2016)

    Google Scholar 

  5. IBM: IBM Watson personality insights has a new model: support of shorter text and precision improvement. Technical report, IBM (2016)

    Google Scholar 

  6. IBM: Personality insight basics. Technical report, IBM (2016)

    Google Scholar 

  7. IBM: Personality models. Technical report, IBM (2016)

    Google Scholar 

  8. Mahmud, J.: IBM Watson personality insights: the science behind the service. Technical report, IBM (2016)

    Google Scholar 

  9. Werlen, L.M.: Statistical learning methods for profiling analysis-notebook for PAN at CLEF 2015. In: Cappellato, L., Ferro, N., Jones, G., Juan, E.S. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, Toulouse, France. CEUR-WS.org, 8–11 September 2015

    Google Scholar 

  10. Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., Juan, E.S. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, Toulouse, France. CEUR-WS.org, 8–11 September 2015

    Google Scholar 

  11. Ṣulea, O.-M., Dichiu, D.: Automatic profiling of Twitter users based on their tweets-notebook for PAN at CLEF 2015. In: Cappellato, L., Ferro, N., Jones, G., Juan, E.S. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, Toulouse, France. CEUR-WS.org, 8–11 September 2015

    Google Scholar 

  12. Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)

    Article  Google Scholar 

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Acknowledgments

We are very grateful to the National Council for Scientific and Technological Development - CNPq (Brazil) for having subsidized the postdoctoral fellowship, and to Saman Daneshvar (University of Ottawa) for revising the paper.

Author information

Authors and Affiliations

  1. University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada

    Romualdo Alves Pereira Junior & Diana Inkpen

Authors
  1. Romualdo Alves Pereira Junior
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  2. Diana Inkpen
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Corresponding author

Correspondence to Romualdo Alves Pereira Junior .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, Saskatchewan, Canada

    Malek Mouhoub

  2. University of Montreal, Montreal, Québec, Canada

    Philippe Langlais

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Pereira Junior, R.A., Inkpen, D. (2017). Using Cognitive Computing to Get Insights on Personality Traits from Twitter Messages. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_32

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  • DOI: https://doi.org/10.1007/978-3-319-57351-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57350-2

  • Online ISBN: 978-3-319-57351-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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