Performance evaluation of SVM with synthetic minority over-sampling technique in sentiment classification - Singgalen, Yerik Afrianto (Institute of Computer Science (IOCS), 2024)
This study investigates the performance of the Support Vector Machine (SVM) algorithm in sentiment analysis tasks within the context of tourism destination branding, utilizing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Specifically, the research compares SVM performance with and without the Synthetic Minority Over-sampling Technique (SMOTE) across various metrics including accuracy, precision, recall, F-measure, and Area Under the Curve (AUC). The analysis is conducted on a dataset comprising textual data extracted from