Travel Vlog Reviews: Support Vector Machine Performance in Sentiment Classification
Yerik Afrianto Singgalen, Sih Yuliana Wahyuningtyas, Yohanes Eko Widodo, Muhamad Nur Agus Dasra, Ruben William Setiawan · Ingénierie des systèmes d information · 2025
This research investigates the combination of the Support Vector Machine algorithm with the Synthetic Minority Over-sampling Technique to improve classification performance in sentiment analysis, especially in handling imbalanced datasets.Employing a dataset comprising 1,928 text entries, the research highlights SVM's challenges in managing imbalanced data, where a predisposition toward the majority class leads to less-than-optimal classification results.Through the application of SMOTE, synthetic samples were generated to balance the minority class, resulting in notable performance improvements, including an accuracy of 83.12%, a precision of 75.76%, a recall of 97.53%, and an Area Under the Curve (AUC) score of 0.978.These outcomes emphasize the effectiveness of integrating SVM and SMOTE to balance class distributions and enhance the model's capacity to distinguish between positive and negative sentiments.The findings underscore the importance of strategic model optimization to achieve balanced results and contribute to advancements in sentiment analysis methodologies.