Quarterly Publication

Document Type : Original Article

Authors

1 Department of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran.

2 Department of Computer Engineering, Rahboard Shomal Institute of Higher Education, Rasht, Iran

3 Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.

10.22105/bdcv.2021.142588

Abstract

Personality can be defined as the combination of behavior, emotion, motivation, and thoughts that aim at describing various aspects of human behavior based on a few stable and measurable characteristics. Considering the fact that our personality has a remarkable influence in our daily life, automatic recognition of a person's personality attributes can provide many essential practical applications in various aspects of cognitive science. Although various methods have been recently proposed for the task of personality recognition, most of them have mainly focused on human-designed statistical features and they did not make use of rich semantic information existing in users' generated texts while not only these contents can demonstrate its writer's internal thought and emotion but also can be assumed as the most direct way for people to state their feeling and opinion in an understandable form. In order to make use of this valuable semantic information as well as overcoming the complexity and handcraft feature requirement of previous methods, a deep learning based method for the task of personality recognition from text is proposed in this paper. Among various deep neural networks, Convolutional Neural Networks (CNN) have demonstrated profound efficiency in natural language processing and especially personality detection. Owing to the fact that various filter sizes in CNN may influence its performance, we decided to combine CNN with AdaBoost, a classical ensemble algorithm, to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter size using AdaBoost. Our proposed method was validated on the Essay dataset by conducting a series of experiments and the empirical results demonstrated the superiority of our proposed method compared to both machine learning and deep learning methods for the task of personality recognition.

Keywords

  1. Han, S., Huang, H., & Tang, Y. (2020). Knowledge of words: an interpretable approach for personality recognition from social media. Knowledge-based systems194, 105550. https://doi.org/10.1016/j.knosys.2020.105550
  2. Schultz, D., & Schultz, S. E. (2015). Psychology and work today: pearson new international edition coursesmart etextbook. Routledge. https://doi.org/10.4324/9781315665009
  3. Xue, D., Wu, L., Hong, Z., Guo, S., Gao, L., Wu, Z., ... & Sun, J. (2018). Deep learning-based personality recognition from text posts of online social networks. Applied intelligence48(11), 4232-4246. https://doi.org/10.1007/s10489-018-1212-4
  4. Majumder, N., Poria, S., Gelbukh, A., & Cambria, E. (2017). Deep learning-based document modeling for personality detection from text. IEEE intelligent systems32(2), 74-79. DOI: 1109/MIS.2017.23
  5. Barrett, H. C. (2020). Towards a cognitive science of the human: cross-cultural approaches and their urgency. Trends in cognitive sciences24(8), 620-638. https://doi.org/10.1016/j.tics.2020.05.007
  6. Frauenstein, E. D., & Flowerday, S. (2020). Susceptibility to phishing on social network sites: a personality information processing model. Computers & security94, 101862. https://doi.org/10.1016/j.cose.2020.101862
  7. Sadr, H., Pedram, M. M., & Teshnehlab, M. (2019). A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks. Neural processing letters50(3), 2745-2761. https://doi.org/10.1007/s11063-019-10049-1
  8. Sadr, H., Pedram, M. M., & Teshnehlab, M. (2020). Multi-view deep network: a deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE access8, 86984-86997. DOI:1109/ACCESS.2020.2992063
  9. Yakhchi, S., Beheshti, A., Ghafari, S. M., & Orgun, M. (2020). Enabling the analysis of personality aspects in recommender systems. arXiv preprint arXiv:2001.04825.
  10. Yang, J. T., Liu, G. M., & Huang, S. C. H. (2020, October). Emotion transformation feature: novel feature for deception detection in videos. 2020 IEEE international conference on image processing (ICIP)(pp. 1726-1730). IEEE. DOI: 1109/ICIP40778.2020.9190846
  11. Nilugonda, M., & Madhavi, K. (2020). A survey on big five personality traits prediction using tensorflow. E3S web of conferences(Vol. 184, p. 01053). EDP Sciences. https://doi.org/10.1051/e3sconf/202018401053
  12. Remaida, A., Abdellaoui, B., Moumen, A., & El Idrissi, Y. E. B. (2020, April). Personality traits analysis using Artificial Neural Networks: a literature survey. 2020 1st international conference on innovative research in applied science, engineering and technology (IRASET)(pp. 1-6). IEEE. DOI: 1109/IRASET48871.2020.9092076
  13. Saxena, A., Khanna, A., & Gupta, D. (2020). Emotion recognition and detection methods: a comprehensive survey. Journal of artificial intelligence and systems2(1), 53-79. https://doi.org/10.33969/AIS.2020.21005
  14. Castellanos, H. A. (2016). Personality recognition applying machine learning techniques on source code metrics. FIRE (Working Notes)(pp. 25-29).
  15. Baig, M. M., Awais, M. M., & El-Alfy, E. S. M. (2017). AdaBoost-based artificial neural network learning. Neurocomputing248, 120-126. https://doi.org/10.1016/j.neucom.2017.02.077
  16. Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: language use as an individual difference. Journal of personality and social psychology77(6), 1296-1312. https://doi.org/10.1037/0022-3514.77.6.1296
  17. Golbeck, J., Robles, C., & Turner, K. (2011). Predicting personality with social media. CHI'11 extended abstracts on human factors in computing systems(pp. 253-262). https://doi.org/10.1145/1979742.1979614
  18. Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011, October). Predicting personality from twitter. 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing(pp. 149-156). IEEE. DOI: 1109/PASSAT/SocialCom.2011.33
  19. Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011, October). Our twitter profiles, our selves: Predicting personality with twitter. 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing(pp. 180-185). IEEE. DOI: 1109/PASSAT/SocialCom.2011.26
  20. Alam, F., Stepanov, E. A., & Riccardi, G. (2013, June). Personality traits recognition on social network-facebook. Seventh international AAAI conference on weblogs and social media.
  21. Skowron, M., Tkalčič, M., Ferwerda, B., & Schedl, M. (2016, April). Fusing social media cues: personality prediction from twitter and instagram. Proceedings of the 25th international conference companion on world wide web(pp. 107-108). https://doi.org/10.1145/2872518.2889368
  22. Li, L., Li, A., Hao, B., Guan, Z., & Zhu, T. (2014). Predicting active users' personality based on micro-blogging behaviors. PloS one9(1), e84997.
  23. Bai, S., Zhu, T., & Cheng, L. (2012). Big-five personality prediction based on user behaviors at social network sites. arXiv preprint arXiv:1204.4809.
  24. Peng, K. H., Liou, L. H., Chang, C. S., & Lee, D. S. (2015, October). Predicting personality traits of Chinese users based on Facebook wall posts. 2015 24th wireless and optical communication conference (WOCC)(pp. 9-14). IEEE. DOI: 1109/WOCC.2015.7346106
  25. Argamon, S., Dhawle, S., Koppel, M., & Pennebaker, J. W. (2005, June). Lexical predictors of personality type. Proceedings of the 2005 joint annual meeting of the interface and the classification society of North America(pp. 1-16).
  26. Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007). Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of artificial intelligence research30, 457-500. https://doi.org/10.1613/jair.2349
  27. Yu, J., & Markov, K. (2017, November). Deep learning based personality recognition from facebook status updates. 2017 IEEE 8th international conference on awareness science and technology (iCAST)(pp. 383-387). IEEE. DOI: 1109/ICAwST.2017.8256484
  28. Tandera, T., Suhartono, D., Wongso, R., & Prasetio, Y. L. (2017). Personality prediction system from facebook users. Procedia computer science116, 604-611. https://doi.org/10.1016/j.procs.2017.10.016
  29. Wang, Z., Wu, C. H., Li, Q. B., Yan, B., & Zheng, K. F. (2020). Encoding text information with graph convolutional networks for personality recognition. Applied sciences10(12), 4081. https://doi.org/10.3390/app10124081
  30. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems(pp. 3111-3119).
  31. Xue, D., Hong, Z., Guo, S., Gao, L., Wu, L., Zheng, J., & Zhao, N. (2017). Personality recognition on social media with label distribution learning. IEEE access5, 13478-13488. DOI:1109/ACCESS.2017.2719018
  32. Mohammad, S. M., & Kiritchenko, S. (2015). Using hashtags to capture fine emotion categories from tweets. Computational intelligence31(2), 301-326. https://doi.org/10.1111/coin.12024
  33. Sun, X., Liu, B., Cao, J., Luo, J., & Shen, X. (2018, May). Who am I? personality detection based on deep learning for texts. 2018 IEEE international conference on communications (ICC)(pp. 1-6). IEEE. DOI: 1109/ICC.2018.8422105