Optimization for Automatic Personality Recognition on Twitter in Bahasa Indonesia
Gabriel Yakub N.N. Adi, Michael Harley Tandio, Veronica Ong, Derwin Suhartono · Procedia Computer Science · 2018
This paper presents optimization techniques for automatic personality recognition (APR) based on Twitter in Bahasa Indonesia, the mother tongue of Indonesians. Foremost, we discuss Twitter and its utilization as a resource for many types of research. Several previous studies have been attempted to predict users’ personality automatically. However, only a few of them have done their research for Bahasa Indonesia data. Therefore, this paper discusses the optimization of APR in Bahasa Indonesia. We evaluate a series of techniques implementing hyperparameter tuning, feature selection, and sampling to improve the machine learning algorithms used. The personality prediction system is built on machine learning algorithms. There are three machine learning algorithms used in this study, namely Stochastic Gradient Descent (SGD), and two ensemble learning algorithms, Gradient Boosting (XGBoost), and stacking (super learner). By implementing this series of optimization techniques, the current study’s evaluation results show huge improvement by achieving 1.0 ROC AUC score with SGD and Super Learner.