Evaluation of the Effectiveness of SMOTE and Random Under Sampling in Emotion Classification of Tweets - Dharmendra, I Komang; Putra, I Made Agus Wirahadi; Atmojo, Yohanes Priyo (INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics, 2024)
This study evaluates the effectiveness of two sampling techniques, SMOTE (Synthetic Minority Over-sampling Technique) and Random Under Sampling (RUS) , in improving the performance of several classification models, namely Maximum Entropy , SVM , Random Forest , Neural Network , and Naive Bayes Classification , for handling data imbalance in emotion classification of tweets. The analysis results show that SMOTE consistently provides a more significant improvement in accuracy, precision, recall, and F1-score compared to RUS , especially in Random Forest and Neural Network models. Maximum Entropy and SVM prove to be the best-performing models in both scenarios, while Naive Bayes Classification , although efficient in terms of time, shows lower performance in evaluation metrics. Overall, SMOTE is a more effective sampling technique compared to RUS in handling class imbalance .