Chamadani Faisal Amri
Sri Suryani Prasetyowati
Yuliant Sibaroni



Personality classification is one of the ways in the field of Natural Language Processing (NLP) with a collection of data that can describe the user's personality through input sets of text documents such as status uploads. Social media is one way to interact online that can provide convenience for users, such as interacting, expressing themselves, and expanding friendships. Status posts on social media can be extracted into useful information in the personality classification process. This research performs classification based on how social media users live their lives and make decisions, which is a representation of the "Thinkers/Feelers" and "Judgers/Perceivers" class attributes of the Myers-Briggs Type Indicator (MBTI) model. Researchers are encouraged to develop a personality classification system with feature extraction that can improve system performance. In this research, there are three main experiments conducted, experiments using data with oversample techniques in the Thinker/Feelers (TF) and Judgers/Perceivers (JP) classes provide the best results compared to other experiments with f1-score and accuracy of 92% using the Random Forest classification method and Glove as the extraction feature.


Glove; Personality Classification; Myers-Briggs Type Indicator (MBTI); Random Forest; Social Media;

Full Text:


Article Metrics :


A. Talasbek, A. Serek, M. Zhaparov, S.-M. Yoo, Y.-K. Kim, and G.-H. Jeong, Personality Classifi-cation by Applying k-means Clustering. 2020. doi: 10.1109/ICAIIC48513.2020.9065244.

X. Wang, Y. Sui, K. Zheng, Y. Shi, and S. Cao, Personality classification of social users based on feature fusion, Sensors, vol. 21, no. 20, Oct. 2021, doi: 10.3390/s21206758.

M. Carlyn, An Assessment of the Myers-Briggs Type Indicator, Journal of Personality Assess-ment, vol. 41, no. 5, pp. 461473, 1977, doi: 10.1207/s15327752jpa4105_2.

V. Ong et al., Personality prediction based on Twitter information in Bahasa Indonesia, in Pro-ceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Nov. 2017, pp. 367372. doi: 10.15439/2017F359.

B. Y. Pratama and R. Sarno, Personality classification based on Twitter text using Naive Bayes, KNN and SVM, in Proceedings of 2015 International Conference on Data and Software Engi-neering, ICODSE 2015, Mar. 2016, pp. 170174. doi: 10.1109/ICODSE.2015.7436992.

R. I. Kurnia, Y. D. Tangkuman, and A. S. Girsang, Classification of user comment using word2vec and SVM classifier, International Journal of Advanced Trends in Computer Science and Engineer-ing, vol. 9, no. 1, pp. 643648, 2020, doi: 10.30534/ijatcse/2020/90912020.

Y. B. N. D. Artissa, I. Arsor, and S. A. Faraby, Personality Classification based on Facebook status text using Multinomial Nave Bayes method, 2019. doi: 10.1088/1742-6596/1192/1/012003.

A. Parmar, R. Katariya, and V. Patel, A Review on Random Forest An Ensemble Classifier, pp. 16, 2019, doi: 10.1007/978-3-030-03146-6_86.

J. Pennington, R. Socher, and C. D. Manning, GloVe Global Vectors for Word Representation.

J. Song et al., The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms, pp. 113, Feb. 2021, doi:

M. M. Tadesse, H. Lin, B. Xu, and L. Yang, Personality Predictions Based on User Behavior on the Facebook Social Media Platform, pp. 111, Oct. 2018, doi: 10.1109/ACCESS.2018.2876502.

R. N. Wykole and A. D. Thakare, A REVIEW OF FEATURE EXTRACTION METHODS FOR TEXT CLASSIFICATION, International Journal of Advance Engineering and Research Devel-opment, pp. 14, 2018.

M. Naili, A. H. Chaibi, and H. H. ben Ghezala, Comparative study of word embedding methods in topic, Procedia Computer Science, pp. 110, 2017, doi: 10.1016/j.procs.2017.08.009.

N. Haziqah et al., Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier, 2020. [Online]. Available:

An Implementation and Explanation of the Random Forest in Python, Accessed: Dec. 07, 2021. [Online]. Available: