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Personality Prediction of Social Network Users Using Ensemble and XGBoost

  • Aditi Kunte
  • Suja PanickerEmail author
Conference paper
  • 15 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)

Abstract

Machine learning has gained tremendous attention from researchers recently. It has wide applications in tasks such as prediction and classification. Current work focuses on the effective detection of the personality of social network users. Personality is a combination of one’s thinking and behavior. Having knowledge about personality of a person has many applications in real world such as varied recommendation systems or HR departments. Personality of a person can be better understood by interacting with him/her. Predicting personality using social media is a new approach where direct interaction with people can be eliminated and accurate predictions can be built. Although different machine learning methods have been used by researchers recently for the task of prediction, the use of Ensembles has not been explored. Current work focuses on advanced classifiers such as XGBoost and Ensemble for prediction. Experimentation on the real-time Twitter dataset indicates high accuracy of 82.59% with an Ensemble. These results are encouraging for future research.

Keywords

Personality prediction Social media Machine learning Psychological tests 

References

  1. 1.
    Dandannavar, P.S., Mangalwede, S.R., Kulkarni, P.M.: Social media text—a source for personality prediction. International Conference on Computational Techniques, Electronics and Mechanism Systems (CTEMS) (2018)Google Scholar
  2. 2.
    Lima, A.C.E.S., de Castro, L.N.: Predicting temperament from Twitter data. In: International Congress on Advanced Applied Informatics (2016)Google Scholar
  3. 3.
    Lukito, L.C., Erwin, A., Purnama, J., Danoekoesoemo, W.: Social media user personality classification using computational linguistic. In: 8th International Conference on Information Technology and Electrical Engineering (ICITEE) (2016)Google Scholar
  4. 4.
    Ahmad, N., Siddique, J.: Personality assessment using Twitter Tweets. In: 21st International Conference on Knowledge Based and Intelligent Information and Engineering Systems (2017)Google Scholar
  5. 5.
  6. 6.
    Al-Samarraie, H., Eldenfria, A., Dawoud, H.: The impact of personality traits on users’ information-seeking behavior. Inf. Process. Manag. 53(1), 237–247 (2017)CrossRefGoogle Scholar
  7. 7.
    Vioulès, MJ, Moulahi, B., Azè, J., Bringay, S.: Detection of suicide-related posts in Twitter data streams. IBM J. Res. Dev. 62(1), 7–1 (2018)Google Scholar
  8. 8.
    Maheswari, K., Packia, P., Priya, A.: Predicting customer behaviour in online shopping using SVM classifier. In: 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (2017)Google Scholar
  9. 9.
    Adi, G.Y.N., Tandio, M.H., Ong, V., Suhartono, D.: Optimization for automatic personality recognition on Twitter in Bahasa Indonesia. In: 3rd International Conference on Computer Science and Computational Intelligence, pp. 473–480 (2018)Google Scholar
  10. 10.
    Krismayer, T., Schedl, M., Knees, P., Rabiser, R.: Predicting user demographics from music listening information. Multimed. Tools Appl. 78(3), 2897–2920 (2019)CrossRefGoogle Scholar
  11. 11.
    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 Adap. Inter. 109–142 (2016)Google Scholar
  12. 12.
    Guntuku, S.C., Qiu, L., Roy, S., Lin, W., Jakhetiya, V.: Do others perceive you as you want them to? Modeling personality based on selfies. In: Proceedings of the 1st international workshop on Affect and sentiment in multimedia, pp. 21–26. Brisbane, Australia (2015)Google Scholar
  13. 13.
    Farnadi, G., Tang, J., De Cock, M., Moens, M.-F.: User profiling through deep multimodal fusion 5–9 (2018)Google Scholar
  14. 14.
    Azucar, D., Marengo, D., Settanni, M.: Predicting the Big 5 personality traits from digital footprints on social media: a meta-analysis. Personal. Individ. Differ. Comput. Hum. Behav. 150–159 (2018)Google Scholar
  15. 15.
    Tran, T., Nguyeny, D., Nguyeny, A., Golenz, E.: Sentiment analysis of Emoji-based reactions on Marijuana-related topical posts on Facebook. In: IEEE International Conference on Communications (ICC) (2018)Google Scholar
  16. 16.
    Fornacciari, P., Mordonini, M., Poggi, A., Sani, L., Tomaiuolo, M.: A holistic system for troll detection on Twitter. Comput. Hum. Behav. (2018)Google Scholar
  17. 17.
    Akhtara, R., Winsboroughb, D., Ortd, U., Johnsonb, A., Chamorro-Premuzic, T.: Detecting the dark side of personality using social media status updates. Personal. Individ. Differ. 90–97 (2018)Google Scholar
  18. 18.
    Tato, A., Nkambou, R., Frasson, C.: Predicting emotions from multimodal users’ data (2018)Google Scholar
  19. 19.
    Panicker, S., Kunte, A.: Personality prediction using social media. In: 5th International Conference for Convergence in Technology (I2CT) (2019)Google Scholar
  20. 20.
    Tandera, T., Suhartono, D., Wongso, R., Prasetio, Y.L.: Personality prediction system from Facebook users. In: 2nd International Conference on Computer Science and Computational Intelligence (2017)Google Scholar
  21. 21.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringDr. Vishwanath Karad MIT World Peace UniversityPuneIndia

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