A Systematic Literature Review of Personality Trait Classification from Textual Content

Hussain Ahmad 2 , Muhammad Zubair Asghar 1 , Alam Sher Khan 2  and Anam Habib 2
  • 1 Institute of Computing and Information Technology, Gomal University, D.I. Khan and 29220, Pakistan
  • 2 Institute of Computing and Information Technology, Gomal University, D.I. Khan and 29220, Pakistan


The day-to-day use of digital devices with Internet access, such as tablets and smartphones, has increased exponentially in recent years and this has had a consequent effect on the usage of the Internet and social media networks. When using social networks, people share personal data that is broadcast between users, which provides useful information for organizations. This means that characterizing users through their social media activity is an emerging research area in the field of Natural Language Processing (NLP) and this paper will present a review of how personality can be detected using online content.

Approach A systematic literature review identified 30 papers published between 2007 and 2019, while particular inclusion and exclusion criteria were used to select the most relevant articles.

Outcomes This review describes a variety of challenges and trends, as well as providing ideas for the direction of future research. In addition, personality trait identification and techniques were classified into different types, including deep learning, machine learning (ML) and semi-supervised/hybrid.

Implications This paper’s outcomes will not only facilitate insight into the various personality types and models but will also provide knowledge about the relevant detection techniques.

Novelty While prior studies have conducted literature reviews in the personality trait detection field, the systematic literature review in this paper provides specific answers to the proposed research questions. This is novel to this field as this particular type of study has not been conducted before.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] Majumder N., Poria S., Gelbukh, A., Cambria E., Deep Learning-Based Document Modeling for Personality Detection from Text, IEEE Intelligent Systems, 2017, 32(2), 74-79

  • [2] Xue D., Hong Z., Guo S., Gao L., Wu L., Zheng J., Zhao N., Personality recognition on social media with label distribution learning. IEEE Access, 2017, 5, 13478-13488

  • [3] Shaffer D., Schwab-Stone M., Fisher P., Preparation, field testing, interrater reliability and acceptability of the DIS-C. Journal of the American Academy of Child & Adolescent Psychiatry (J Am Acad Child Adolesc Psychiatry), 1993, 32, 643-648

  • [4] Myers I., Myers P., Gifts differing, Palo Alto: Consulting Psychologists Press, 1990

  • [5] Goldberg L. R., An Alternative “Description of Personality”: The Big-Five Factor Structure. Personality and Personality Disorders: The Science of Mental Health, 2013, 7, 34

  • [6] Bharadwaj S., Sridhar S., Choudhary R., Srinath R., Persona Traits Identification based on Myers-Briggs Type Indicator (MBTI)-A Text Classification Approach, Proceeding of International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018, 1076-1082.

  • [7] Kaushal V., Patwardhan M., Emerging trends in personality identification using online social networks—a literature survey. ACM Transactions on Knowledge Discovery from Data (TKDD), 2018, 12(2), 15

  • [8] Keele S., Guidelines for performing systematic literature reviews in software engineering. Technical report, Ver. 2.3 EBSE Technical Report. EBSE, 2007), 5

  • [9] Vinciarelli A., Mohammadi G., A survey of personality computing. IEEE Transactions on Affective Computing, 2014, 5(3), 273-291

  • [10] Mairesse F., Walker M. A., Mehl M. R., Moore R. K., Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of artificial intelligence research (JAIR), 2007, 30, 457-500

  • [11] Robbins S. P., Judge T., Essentials of organizational behaviour, 15 Edition, 2012

  • [12] Allport G. W., Pattern and growth in personality, 1961

  • [13] Cattell R. B., Eber H. W., Tatsuoka M. M., Handbook for the sixteen personality factor questionnaire (16 PF): In clinical, educational, industrial, and research psychology, for use with all forms of the test. Institute for Personality and Ability Testing, 1970

  • [14] Pittenger D. J., The utility of the Myers-Briggs type indicator. Review of Educational Research (RER), 1993, 63(4), 467-488

  • [15] Schwartz S. H., Basic human values: Theory, measurement, and applications. Revue française de sociologie, 2007, 47(4), 929

  • [16] Noftle E. E., Robins R. W., Personality predictors of academic outcomes: big five correlates of GPA and SAT scores. Journal of personality and social psychology (J. Pers. Soc. Psychol.), 2007, 93(1), 116

  • [17] Shivakumar G., Vijaya P. A., Facial Expression Based Human Emotion Recognition with Live Computer Response. International Journal of computer science and information technology (IJCSIT), 2011, 81-84

  • [18] Chaudhary S., Sing R., Hasan S. T., Kaur I., A comparative Study of Different Classifiers for Myers-Brigg Personality Prediction Model, IRJET, 2018, 05, 1410-1413

  • [19] Gjurković M., Šnajder J., Reddit: A Gold Mine for Personality Prediction, Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, 2018, 87-97

  • [20] Kaur P., Gosain A., Comparing the Behavior of Oversampling and Undersampling Approach of Class Imbalance Learning by Combining Class Imbalance Problem with Noise. In ICT Based Innovations, Springer, Singapore, 2018, 23-30

  • [21] Arroju M., Hassan A., Farnadi G., Age, gender and personality recognition using tweets in a multilingual setting. In 6th Conference and Labs of the Evaluation Forum (CLEF 2015): Experimental IR meets multilinguality, multimodality, and interaction, 2015, 23-31

  • [22] Alam F., Stepanov E. A., Riccardi G., Personality traits recognition on social network-facebook. In Seventh International AAAI Conference on Weblogs and Social Media. (ICWSM-13), Cambridge, MA, USA, 2013

  • [23] Kedar S., Nair V., Kulkarni S., Personality identification through handwriting analysis: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng, 2015, 5(1)

  • [24] Ilimini K., Fernando T. G. I., Persons’ personality traits recognition using machine learning algorithms and image processing techniques. Advances in Computer Science: an International Journal, 2016, 5(1), 40-44

  • [25] Liu L., Preotiuc-Pietro D., Samani Z. R., Moghaddam M. E, Ungar L. H., Analyzing Personality through Social Media Profile Picture Choice. In Tenth international AAAI conference on web and social media (ICWSM), 2016, 211-220.

  • [26] Sagadevan S., Malim N. H. A. H., Husin M. H., Sentiment Valences for Automatic Personality Detection of Online Social Networks Users Using Three Factor Model. Procedia Computer Science, 2015, 72, 201-208

  • [27] Pratama B. Y., Sarno R, Personality classification based on Twitter text using Naive Bayes, KNN and SVM. In Data and Software Engineering (ICoDSE), 2015 International Conference, IEEE, 2015, 170-174

  • [28] Ong, V., Rahmanto A. D., Williem, Suhartono D., Exploring Personality Prediction from Text on Social Media: A Literature Review. INTERNETWORKING INDONESIA, 2017, 9(1), 65-70

  • [29] Buraya K., Farseev A., Filchenkov A., Chua T. S., Towards User Personality Profiling from Multiple Social Networks. In Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 2017, 4909-4910

  • [30] Ngatirin N. R., Zainol Z., Yoong T. L. C., A comparative study of different classifiers for automatic personality prediction. In Control System, Computing and Engineering (ICCSCE), 2016 6th IEEE International Conference, IEEE, 2016, 435-440

  • [31] Sewwandi D., Perera K., Sandaruwan S., Lakchani O., Nugaliyadde A., Thelijjagoda S., Linguistic features based personality recognition using social media data. In Technology and Management (NCTM), National Conference, IEEE, 2017, 63-68

  • [32] Poria S., Gelbukh A., Agarwal B., Cambria E., Howard N., Common sense knowledge based personality recognition from text. In Mexican International Conference on Artificial Intelligence, Springer, Berlin, Heidelberg, 2013, 484-496

  • [33] Asra S., Shubhangi D. C., Personality Trait Identification Using Unconstrained Cursive and Mood Invariant Handwritten Text. International Journal of Education and Management Engineering, 2015, 5(5), 20

  • [34] Celli F., Unsupervised personality recognition for social network sites. In Proc. of Sixth International Conference on Digital Society, 2012, 59-62

  • [35] Celli F., Rossi L., The role of emotional stability in Twitter conversations. In Proceedings of the workshop on semantic analysis in social media, Association for Computational Linguistics, 2012, 10-17

  • [36] Celli F., Mining user personality in twitter. Language, Interaction and Computation CLIC, 2011

  • [37] Kafeza E., Kanavos A., Makris C., Vikatos P., T-PICE: Twitter personality based influential communities extraction system. In Big Data (BigData Congress), 2014 IEEE International Congress, IEEE, 2014, 212-219

  • [38] Sun X., Liu B., Meng Q., Cao J., Luo J., Yin H., Group-level personality detection based on text generated networks. World Wide Web, 2019, 1-20

  • [39] Chishti S., Li X., Sarrafzadeh A., Identify Website Personality by Using Unsupervised Learning Based on Quantitative Web-site Elements. In International Conference on Neural Information Processing, Springer, Cham, 2015, 522-530

  • [40] Kramer R. S., King J. E., Ward R., Identifying personality from the static, nonexpressive face in humans and chimpanzees: evidence of a shared system for signaling personality. Evolution and Human Behavior, 2011, 32(3), 179-185

  • [41] Lukito L C., Erwin A., Purnama J., Danoekoesoemo W., Social media user personality classification using computational linguistic. In Information Technology and Electrical Engineering (ICITEE), 2016 8th International Conference, IEEE, 2016, 1-6

  • [42] Alsadhan N., Skillicorn D., Estimating Personality from Social Media Posts. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, 2017, 350-356.

  • [43] Bai S., Zhu T., Cheng L., Big-five personality prediction based on user behaviors at social network sites. arXiv preprint arXiv:1204.4809, 2012

  • [44] Ahmad S., Asghar M. Z., Alotaibi F. M., Awan I., Detection and classification of social media-based extremist aflliations using sentiment analysis techniques. Human-centric Computing and Information Sciences, (2019), 9(1), 24

  • [45] Xue D., Wu L., Hong Z., Guo S., Gao L., Wu Z. et al., Deep learning-based personality recognition from text posts of online social networks. Applied Intelligence, 2018, 48(11), 4232-4246

  • [46] Yun, W., An W. X., Jindan Z., Yu C., Combining vector space features and convolution neural network for text sentiment analysis. In Conference on Complex, Intelligent, and Software Intensive Systems, Springer, Cham, 2018, 780-790.

  • [47] Hernandez R K., Scott L., Predicting Myers-Briggs type indicator with text, In 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017

  • [48] Arnoux P. H., Xu A., Boyette N. Mahmud J., Akkiraju R., Sinha V., 25 Tweets to Know You: A New Model to Predict Personality with Social Media. In Eleventh International AAAI Conference on Web and Social Media (ICWSM 2017), 2017

  • [49] Liu F., Perez J., Nowson S., (2016): A language-independent and compositional model for personality trait recognition from short texts. arXiv preprint arXiv:1610.04345

  • [50] Sumner C., Byers A., Boochever R., Park G. J., Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets. In 2012 11th International Conference on Machine Learning and Applications, IEEE, 2012, 2 386-393

  • [51] Sharma K., Kaur A., Personality prediction of Twitter users with Logistic Regression Classifier learned using Stochastic Gradient Descent. IOSR Journal of Computer Engineering (ISOR-JCE), 2015, 17(4), 39-47

  • [52] Yang Z., Wang C., Zhang F., Zhang Y., Zhang H., Emerging rumor identification for social media with hot topic detection. In Web Information System and Application Conference (WISA), 2015 12th, IEEE, 2015, 53-58

  • [53] Chawla N. V., Bowyer K. W., Hall L. O., Kegelmeyer W. P., SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 2002, 16, 321-357

  • [54] Carducci G., Rizzo G., Monti D., Palumbo E., Morisio M., TwitPersonality: Computing personality traits from tweets using word embeddings and supervised learning. Information, 2018, vol. 9, no. 5, pp. 127.

  • [55] Chin D. N., Wright W.R., Social Media Sources for Personality Profiling. In Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization, Aalborg, Denmark, 2014, 1181, 79–85

  • [56] Golbeck J., Robles C., Turner K., Predicting personality with social media. In Proceedings of the CHI ’11 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’11), Vancouver, BC, Canada, 2011, 10, 253–262.

  • [57] Rosen P. A., Kluemper D., The impact of the Big Five personality traits on the acceptance of social networking website. In Proceedings of the Americas Conference on Information Systems (AMCIS 2008), Toronto, ON, Canada, 2008, 274


Journal + Issues

Open Computer Science is an open access, peer-reviewed journal. The journal publishes research results in the following fields: algorithms and complexity theory, artificial intelligence, bioinformatics, networking and security systems,
programming languages, system and software engineering, and theoretical foundations of computer science.