Document Type : Applied Article


School of Computer engineering, Iran University of Science and Technology, Tehran, Iran.


Determining the personality dimensions of individuals is very important in psychological research. The most well-known example of personality dimensions is the Five-Factor Model (FFM). There are two approaches 1- Manual and 2- Automatic for determining the personality dimensions. In a manual approach, Psychologists discover these dimensions through personality questionnaires. As an automatic way, varied personal input types (textual/image/video) of people are gathered and analyzed for this purpose. In this paper, we proposed a method called DENOVA (DEep learning based on the ANOVA), which predicts FFM using deep learning based on the Analysis of variance (ANOVA) of words. For this purpose, DENOVA first applies ANOVA to select the most informative terms. Then, DENOVA employs Word2Vec to extract document embeddings. Finally, DENOVA uses Support Vector Machine (SVM), Logistic Regression, XGBoost, and Multilayer perceptron (MLP) as classifiers to predict FFM. The experimental results show that DENOVA outperforms on average, 6.91%, the state-of-the-art methods in predicting FFM with respect to accuracy.


[1]M. S. Salem, S. S. Ismail, M. Aref, "Personality Traits for Egyptian Twitter Users Dataset," in ACM, 2019.
[2] P. H. Arnoux, A. Xu, N. Boyette, J. Mahmud, R. Akkiraju, V. Sinha, "25 Tweets to Know You: A New Model to Predict Personality with Social Media," in Eleventh International AAAI Conference on Web and Social Media, 2017.
[3] E. P. Tighe, J. C. Ureta, B. A. Pollo, C. K. Cheng, R. de Dios Bulos, "Personality Trait Classification of Essays with the Application of Feature Reduction," in 4th Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2016), IJCAI 2016, New York City, USA, 2016.
[4] N. Taghvaei, B. Masoumi, M. R. Keyvanpour, "A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks," Journal of AI and Data Mining, 2021.
[5] S. Arjaria, A. Shrivastav, A. S. Rathore, V. Tiwari, "Personality Trait Identification for Written Texts Using MLNB," in Data, Engineering and Applications, Singapore, Springer, 2019, pp. 131-137.
[6] D. R. Moreno, J. C. Gomez, D. L. Almanza-Ojeda, M. A. Ibarra-Manzano, "Prediction of Personality Traits in Twitter Users with Latent Features," in IEEE, 2019.
[7] M. A. Nunes, R. Hu, "Personality-based recommender systems: an overview," in Proceedings of the sixth ACM conference on Recommender systems, 2012.
[8] R. R. McCrae, O. P. John, "An introduction to the five‐factor model and its applications," Journal of personality, vol. 60, pp. 175-215, 1992.
[9] T. T. Nguyen, F. M. Harper, L. Terveen, J. A. Konstan, "User personality and user satisfaction with recommender systems," Information Systems Frontiers, 2018.
[10] G. Nave, J. Minxha, D. M. Greenberg, M. Kosinski, D. Stillwell, J. Rentfrow, "Musical preferences predict personality: evidence from active listening and facebook likes," Psychological Science, 2018.
[11] R. Gao, B. Hao, S. Bai, L. Li, A. Li, T. Zhu, "Improving user profile with personality traits predicted from social media content," Proceedings of the 7th ACM conference on recommender systems, 2013.
[12] C. Aydogmus, S. M. Camgoz, A. Ergeneli, O. T. Ekmekci, "Perceptions of transformational leadership and job satisfaction: The roles of personality traits and psychological empowerment," 2018.
[13] P. Steel, J. Schmidt, F. Bosco, K. Uggerslev, "The effects of personality on job satisfaction and life satisfaction: A meta-analytic investigation accounting for bandwidth{fidelity and commensurability," Human Relations, 2019.
[14] K. M. Beaver, J. C. Boutwell BB, Barnes, M. G. Vaughn, M. DeLisi, "The association between psychopathic personality traits and criminal justice outcomes: Results from a nationally representative sample of males and females," Crime Delinquency, 2017.
[15] S. G. Van de Weijer, E. R. Leukfeldt, "Big five personality traits of cybercrime victims," Cyberpsychology, Behavior, and Social Networking, 2017.
[16] P. T. Costa, R. R. McCrea, "Revised neo personality inventory (neo pi-r) and neo five-factor inventory (neo-ffi)," Psychological Assessment Resources, 1992.
[17] O. P. John, E. M. Donahue, R. L. Kentle, "The big five inventory—versions 4a and 54," 1991.
[18] L. R. Goldberg, J. A. Johnson, H. W. Eber, R. Hogan, M. C. Ashton, C. R. Cloninger, H. G. Gough, "The international personality item pool and the future of public-domain personality measures," Journal of Research in personality, 2006.
[19] N. Majumder, S. Poria, A. Gelbukh, E. Cambria, "Deep learning-based document modeling for personality detection from text," IEEE Intelligent Systems, 2017.
[20] E. Tighe, C. Cheng, "Modeling Personality Traits of Filipino Twitter Users," in 2nd Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES 2018), NAACL HLT 2018, New Orleans, Louisiana, USA, 2018.
[21] I. Wilf, Y. Michaeli, S. Gilboa, D. H. Gavriel, G. Bechar, "Method and system for predicting personality traits, capabilities and suggested interactions from images of a person," United States patent application US, 2019.
[22] M. Cristani, A. Vinciarelli, C. Segalin, A. Perina, "Unveiling the multimedia unconscious: Implicit cognitive processes and multimedia content analysis," Proceedings of the 21st ACM international conference on Multimedia, 2013.
[23] F. Valente, S. Kim, P. Motlicek, "Annotation and recognition of personality traits in spoken conversations from the ami meetings corpus," Thirteenth annual conference of the international speech communication association, 2012.
[24] T. Polzehl, S. Möller, F. Metze, "Automatically assessing personality from speech," in IEEE Fourth International Conference on Semantic Computing, 2010.
[25] S. I. Levitan, Y. Levitan, G. An, M. Levine, R. Levitan, A. Rosenberg, J. Hirschberg, "Identifying individual differences in gender, ethnicity, and personality from dialogue for deception detection," Proceedings of the second workshop on computational approaches to deception detection, 2016.
[26] L. F. Gallardo, B. Weiss, "Towards Speaker Characterization: Identifying and Predicting Dimensions of Person Attribution," INTERSPEECH, 2017.
[27] Y. Mehta, N. Majumder, A. Gelbukh, E. Cambria, "Recent trends in deep learning based personality detection," Artificial Intelligence Review, 2019.
[28] D. J. Hughes, M. Rowe, M. Batey, A. Lee, "A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage," Computers in Human Behavior, 2013.
[29] S. Poria, A. Gelbukh, B. Agarwal, E. Cambria, N. Howard, "Common sense knowledge based personality recognition from text," in Mexican International Conference on Artificial Intelligence, 2013.
[30] R. R. McCrae, P. T. Costa, "Validation of the five-factor model of personality across instruments and observers," Journal of personality and social psychology, 1987.
[31] J. W. Pennebaker, L. A. King, "Linguistic styles: Language use as an individual difference," Journal of personality and social psychology, 1999.
[32] S. M. Mohammad, P. D. Turney, "Crowdsourcing a word–emotion association lexicon," Computational Intelligence, 2013.
[33] F. Mairesse, M. A. Walker, M. R. Mehl, R. K. Moore, "Using linguistic cues for the automatic recognition of personality in conversation and text," Journal of artificial intelligence research, 2007.
[34] J. W. Pennebaker, R. L. Boyd, K. Jordan, K. Blackburn, "The development and psychometric properties of LIWC2015," 2015.
[35] M. Coltheart, "The MRC psycholinguistic database," The Quarterly Journal of Experimental Psychology Section A, 1981.
[36] C. Havasi, R. Speer, J. Alonso, "ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge," Recent advances in natural language processing 2007, 2007.
[37] S. Poria, A. Gelbukh, A. Hussain, N. Howard, D. Das, S. Bandyopadhyay, "Enhanced SenticNet with affective labels for concept-based opinion mining," IEEE Intelligent Systems, 2013.
[38] T. Tandera, D. Suhartono, R. Wongso, Y. L. Prasetio, "Personality Prediction System from Facebook Users," Procedia Computer Science, 2017.
[39] M. Kosinski, S. C. Matz, S. D. Gosling, V. Popov, D. Stillwell, "Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines," American Psychologist, 2015.
[40] K. Moffitt, J. Giboney, E. Ehrhardt, J. K. Burgoon, J. F. Nunamaker, "Structured programming for linguistic cue extraction," The Center for the Management of Information, 2010.
[41] M. M. Tadesse, H. Lin, B. Xu, L. Yang, "Personality predictions based on user behavior on the facebook social media platform," IEEE Access, 2018.
[42] S. Nowson, J. Oberlander, "The Identity of Bloggers: Openness and gender in personal weblogs," in AAAI spring symposium: Computational approaches to analyzing weblogs, 2006.
[43] IB. Drexel, "Feature Engineering and Word Embedding Impacts for Automatic Personality Detection on Instant Message," in 2019 International Conference on Information Management and Technology (ICIMTech), 2019.
[44] P. Bojanowski, E. Grave, A. Joulin, T. Mikolov, "Enriching word vectors with subword information," Transactions of the Association for Computational Linguistics, 2017.
[45] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, "Fasttext. zip: Compressing text classification models," arXiv preprint, 2016.
[46] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, "Bag of tricks for efficient text classification," arXiv preprint, 2016.
[47] J. Philip, D. Shah, S. Nayak, S. Patel, Y. Devashrayee, "Machine learning for personality analysis based on big five model," in Data Management, Analytics and Innovation, Singapore, Springer, 2019, pp. 345-355.
[48] C. Fellbaum, "WordNet," The encyclopedia of applied linguistics, 2012.
[49] H. Zheng, C. Wu, "Predicting Personality Using Facebook Status Based on Semi-supervised Learning," in Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 2019.
[50] E. Loper, S. Bird, "NLTK: the natural language toolkit," arXiv preprint cs/0205028, 2002.
[51] J. Perkins, Python 3 text processing with NLTK 3 cookbook, Packt Publishing Ltd, 2014.
[52] N. Green, P. Breimyer, V. Kumar, N. Samatova, "WebBANC: Building Semantically-Rich AnnotatedCorpora from Web User Annotations of Minority Languages," in WebBANC: Building Semantically-Rich AnnotatedCorpora from Web User Annotations of Minority Languages, 2009.
[53] T. Bergmanis, S. Goldwater, "Context sensitive neural lemmatization with lematus," in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018.
[54] L. St, S. Wold, "Analysis of variance (ANOVA)," Elsevier, 1989.
[55] "Google Code Archive," Google, 2013. [Online]. Available:
[56] M. Mihaltz, 2016. [Online]. Available:
[57] C. Cortes, V. Vapnik, "Support-vector networks," Machine learning, 1995.
[58] RE. Wright, "Logistic regression".