Abstract
The Myers-Briggs Type Indicator (MBTI) is typifies personality on the basis of four basic dichotomy traits. It has been used by psychologists with diverse applications in real life and clinical settings. Recently there are attempts to carry out MBTI indexing by Machine Learning (ML) techniques applied to several kinds of signals among them textual data extracted from interactions in social networks. In this paper we apply a battery of well known ML approaches to the prediction of MBTI categories based on features extracted by natural language processing (NLP) techniques from textual data extracted from a social network devoted to personality evaluation. The results are in agreement with the literature, showing that prediction of MBTI personality indicator is highly reproducible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adamopoulos, P., Ghose, A., Todri, V.: The impact of user personality traits on word of mouth: text-mining social media platforms. Inf. Syst. Res. 29(3), 612–640 (2018)
Bharadwaj, S., Sridhar, S., Choudhary, R., Srinath, R.: Persona traits identification based on Myers-Briggs type indicator (MBTI)-a text classification approach. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1076–1082. IEEE (2018)
En Jun Choong and Kasturi Dewi Varathan: Predicting judging-perceiving of Myers-Briggs type indicator (MBTI) in online social forum. PeerJ 9, e11382 (2021)
Ezpeleta, E., Garitano, I., Arenaza-Nuño, I., Hidalgo, J.M.G., Zurutuza, U.: Novel comment spam filtering method on Youtube: sentiment analysis and personality recognition. In: Garrigós, I., Wimmer, M. (eds.) ICWE 2017. LNCS, vol. 10544, pp. 228–240. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74433-9_21
Genina, A., Gawich, M., Hegazy, A.: An approach for sentiment analysis and personality prediction using Myers Briggs type indicator. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds.) AISI 2020. AISC, vol. 1261, pp. 179–186. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58669-0_16
Górriz, J.M., Álvarez-Illán, I., Álvarez-Marquina, A., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Inf. Fusion 100, 101945 (2023)
Jaysundara, A., De Silva, D., Kumarawadu, P.: Personality prediction of social network users using LSTM based sentiment analysis. In: 2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), pp. 1–6. IEEE (2022)
Komisin, M.C., Guinn, C.: Identifying personality types using document classification methods. PhD thesis, University of North Carolina Wilmington Wilmington (2011)
Lin, J., Mao, W., Zeng, D.D.: Personality-based refinement for sentiment classification in microblog. Knowl.-Based Syst. 132, 204–214 (2017)
Liu, B.: Sentiment analysis and opinion mining. Springer Nature (2022)
Quenk, N.L.: Essentials of Myers-Briggs Type Indicator Assessment. Wiley, Hoboken (2009)
Wu, H.C., Luk, R.W.P., Wong, K.F., Kwok, K.L.: Interpreting TF-IDF term weights as making relevance decisions. ACM Trans. Inf. Syst. 26(3), 1–37 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Morais-Quilez, I., Graña, M., de Lope, J. (2024). Machine Learning for Personality Type Classification on Textual Data. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-61140-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61139-1
Online ISBN: 978-3-031-61140-7
eBook Packages: Computer ScienceComputer Science (R0)