Semantic Approach for Big Five Personality Prediction on Twitter
Dublin Core
Title
Semantic Approach for Big Five Personality Prediction on Twitter
Subject
Big Five Personality, BERT, SVM, LIWC
Description
Personality provides a deep insight of someone and has an important part in someone’s job performance.
Predicting personality through social media has been studied on several research. The problem is how to
improve the performance of personality prediction system. The purpose of this research is to predict personality
on Twitter users and increase the performance of the personality prediction system. An online survey using Big
Five Inventory (BFI) questionnaire has been distributed and gathered 295 Twitter users with 511,617 tweets
data. In this research, we experiment on two different methods using Support Vector Machine (SVM), and the
combination of SVM and BERT as the semantic approach. This research also implements Linguistic Inquiry
Word Count (LIWC) as the linguistic feature for personality prediction system. The results showed that
combination of these two methods achieve 79.35% accuracy score and with the implementation of LIWC can
improve the accuracy score up to 80.07%. Overall, these results showed that the combination of SVM and BERT
as the semantic approach with the implementation of LIWC is recommended to gain a better performance for the
personality prediction system
Predicting personality through social media has been studied on several research. The problem is how to
improve the performance of personality prediction system. The purpose of this research is to predict personality
on Twitter users and increase the performance of the personality prediction system. An online survey using Big
Five Inventory (BFI) questionnaire has been distributed and gathered 295 Twitter users with 511,617 tweets
data. In this research, we experiment on two different methods using Support Vector Machine (SVM), and the
combination of SVM and BERT as the semantic approach. This research also implements Linguistic Inquiry
Word Count (LIWC) as the linguistic feature for personality prediction system. The results showed that
combination of these two methods achieve 79.35% accuracy score and with the implementation of LIWC can
improve the accuracy score up to 80.07%. Overall, these results showed that the combination of SVM and BERT
as the semantic approach with the implementation of LIWC is recommended to gain a better performance for the
personality prediction system
Creator
Ghina Dwi Salsabila1
, Erwin Budi Setiawan
2
, Erwin Budi Setiawan
2
Publisher
Telkom University
Date
: 20-08-2021
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Ghina Dwi Salsabila1
, Erwin Budi Setiawan
2, “Semantic Approach for Big Five Personality Prediction on Twitter,” Repository Horizon University Indonesia, accessed May 23, 2025, https://repository.horizon.ac.id/items/show/8904.