Implementation of Stacking Ensemble Classifier for Multi-class Classification of COVID-19 Vaccines Topics on Twitter

Abstract

Purpose: However, from the variety of uses of these algorithms, in general, accuracy problems are still a concern today, even accuracy problems related to multi-class classification still require further research. Methods: This study proposes a stacking ensemble classifier method to produce better accuracy by combining Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms as first-level learners and using Logistic Regression as a meta-learner for the multi-class classification of COVID-19 vaccine topics on Twitter. Result: Based on the evaluation, the proposed Stacking Ensemble Classifier model shows 86% accuracy, 85% precision, 86% recall, and 85% f1-score. Novelty: The novelty is produce better accuracy by combining Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms as first-level learners and using Logistic Regression as a meta-learner.