Engineering, Mathematics and Computer Science Journal (EMACS)
Vol. 6 No. 1 (2024): EMACS

Machine Learning for Predicting Personality using Facebook-Based Posts

Suhartono, Derwin (Unknown)
Ciputri, Marcella Marella (Unknown)
Susilo, Stefanny (Unknown)



Article Info

Publish Date
31 Jan 2024

Abstract

Social media contributes a lot to human life. People can share their thoughts through text, photos, and voice through social media. Information from social media can be useful, including in personality research. Personality can generally be known through personality tests. In this research, personality prediction is formed to determine personality through Facebook posts without using a personality test. We create a model based on big five personality traits using 5 machine learning algorithms: Support Vector Machine (SVM), Multinomial Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression. Data augmentation was also used for balancing the dataset value and trained using stratified 10-fold cross-validation. This research yields the highest f1 score on Openness using Multinomial Naive Bayes algorithm of 82.31% and the highest average is 68.62%. So the five supervised Machine Learning algorithms used in this research produced Multinomial Naive Bayes as the best algorithm to predict personality based on big five personality traits from user postings on Facebook.

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Journal Info

Abbrev

EMACS

Publisher

Subject

Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

Description

Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food ...