• himaya perera Informatics Institute of Technology
  • Lakshan Costa



Machine Learning, Deep learning, Personality Classification


Personality classification from text is a very popular domain of research among the domain of natural language processing. Personality of an individual has been found to be a very important characteristic when analyzing an individual for a particular purpose. Especially in fields such as e-recruitment, personality is a determining factor of if an individual has a placement at a particular workplace. The author aims to explore various personality classifications such as ‘The Big Five’ and the “Myer Briggs Type Indicator’ and various approaches in which text classification when it comes to detecting personality. Both machine learning and deep learning approaches are examined, and their inner workings, benefits and limitations are detailed as well. We expect that this article will provide a thorough insight to personality classification of text by a numerous number of approaches.


Download data is not yet available.


K. Dumper, “10.1 What is Personality? – Introductory Psychology,”

H. Gillette, “Are You Born with Personality or Does It Develop Later On?,” Psych Central, Jan. 25, 2022.

D. H. M. Pelt, D. van der Linden, C. S. Dunkel, and M. Ph. Born, “The general factor of personality and job performance: Revisiting previous meta-analyses,” International Journal of Selection and Assessment, vol. 25, no. 4, pp. 333–346, Nov. 2017, doi:

F. Schmidt, “The Validity and Utility of Selection Methods in Personnel Psychology: Practical and Theoretical Implications of 100 Years of Research Findings | Request PDF,” ResearchGate, Oct. 2016.

M. Barrick and M. Mount, “THE BIG FIVE PERSONALITY DIMENSIONS AND JOB PERFORMANCE: A META-ANALYSIS,” Personnel Psychology, vol. 44, no. 1, pp. 1–26, Mar. 1991, doi:

Y. Cohen, H. Ornoy, and B. Keren, “MBTI Personality Types of Project Managers and Their Success: A Field Survey,” Project Management Journal, vol. 44, no. 3, pp. 78–87, Jun. 2013, doi:

K. Cherry, “The Big Five Personality Traits,” Verywell Mind, Oct. 19, 2022.

J. Golbeck, C. Robles, and K. Turner, “Predicting personality with social media,” Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems - CHI EA ’11, 2011, doi:

D. Jurafsky and J. Martin, “Speech and Language Processing,”, 2018.

S. Katiyar, S. Kumar, and H. Walia, “Personality Prediction from Stack Overflow by using Naïve Bayes Theorem in Data Mining,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 3, pp. 1555–1559, Jan. 2020, doi:

K. Shah, H. Patel, D. Sanghvi, and M. Shah, “A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification,” Augmented Human Research, vol. 5, no. 1, Mar. 2020, doi:

]M. Z. Islam, J. Liu, J. Li, L. Liu, and W. Kang, “A Semantics Aware Random Forest for Text Classification,” Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Nov. 2019, doi:

B. Y. Pratama and R. Sarno, “Personality classification based on Twitter text using Naive Bayes, KNN and SVM,” 2015 International Conference on Data and Software Engineering (ICoDSE), Nov. 2015, doi:

Y. Wahba, N. Madhavji, and J. Steinbacher, “A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks,” arXiv:2211.02563 [cs], Nov. 2022, Accessed: Mar. 05, 2023. [Online]. Available:

M. Amin and N. Nadeem, “Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System.” Accessed: Mar. 05, 2023. [Online]. Available:

Y. Elazar et al., “Measuring and Improving Consistency in Pretrained Language Models,” Transactions of the Association for Computational Linguistics, vol. 9, pp. 1012–1031, 2021, doi:

J. Duan, H. Zhao, Q. Zhou, M. Qiu, and M. Liu, “A Study of Pre-trained Language Models in Natural Language Processing,” 2020 IEEE International Conference on Smart Cloud (SmartCloud), Nov. 2020, doi:

R. Khusuma, W. Maharani, and P. H. Gani, “Personality Detection On Twitter User With RoBERTa,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, no. 1, pp. 542–553, Feb. 2023, doi:

V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,”, 2019.

R. Soricut, Z. Lan, Research Scientists, and Google Research, “ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations,” Google AI Blog, Dec. 20, 2019.

J. Jacob, “What is FLAN-T5? Is FLAN-T5 a better alternative to GPT-3? |,”, Feb. 02, 2023. (accessed Mar. 05, 2023).

T. Brown et al., “Language Models are Few-Shot Learners,” 2020. Available:

P. Rajapaksha, R. Farahbakhsh, and N. Crespi, “BERT, XLNet or RoBERTa: The Best Transfer Learning Model to Detect Clickbaits,” IEEE Access, vol. 9, pp. 154704–154716, 2021, doi:

Z. Ren, Q. Shen, X. Diao, and H. Xu, “A sentiment-aware deep learning approach for personality detection from text,” Information Processing & Management, vol. 58, no. 3, p. 102532, May 2021, doi:

X. Sun, B. Liu, Q. Meng, J. Cao, J. Luo, and H. Yin, “Group-level personality detection based on text generated networks,” World Wide Web, Sep. 2019, doi:

X. Sun, B. Liu, J. Cao, J. Luo, and X. Shen, “Who Am I? Personality Detection Based on Deep Learning for Texts,” 2018 IEEE International Conference on Communications (ICC), May 2018, doi:

Md. A. Rahman, A. Al Faisal, T. Khanam, M. Amjad, and M. S. Siddik, “Personality Detection from Text using Convolutional Neural Network,” 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), May 2019, doi:

C. Martin, “The Big Five OCEAN Personality Types: Introduction and Discussions,”

N. Majumder, S. Poria, A. Gelbukh, and E. Cambria, “Deep Learning-Based Document Modeling for Personality Detection from Text,” IEEE Intelligent Systems, vol. 32, no. 2, pp. 74–79, Mar. 2017, doi:

A. Kazameini, S. Fatehi, Y. Mehta, S. Eetemadi, and E. Cambria, “Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles,” arXiv:2010.01309 [cs], Oct. 2020, Available:




How to Cite

perera, himaya, & Costa, L. (2023). PERSONALITY CLASSIFICATION OF TEXT THROUGH MACHINE LEARNING AND DEEP LEARNING: A REVIEW (2023). International Journal For Research In Advanced Computer Science And Engineering, 9(4), 6–12.