Enhancing Sentiment Analysis Accuracy in Borobudur Temple Visitor Reviews through Semi-Supervised Learning and SMOTE Upsampling

A sentiment analysis model for Borobudur Temple visitor reviews achieved an accuracy rate of 83.68% after applying the Synthetic Minority Oversampling Technique (SMOTE). This represents a significant improvement over the initial accuracy of 60.59%, demonstrating the effectiveness of SMOTE in enhancing sentiment analysis outcomes. The study employed a semi-supervised learning approach, partitioning data into labeled and unlabeled sets for automatic labeling using the Multinomial Naïve Bayes algorithm. Preprocessing steps included data cleaning, tokenization, and stop word removal. These findings suggest that SMOTE upsampling can substantially refine sentiment analysis in tourist reviews, with potential applications in destination recommendation systems.

Agustina, C., Purwanto, P., & Farikhin, F. (2024). Enhancing Sentiment Analysis Accuracy in Borobudur Temple Visitor Reviews through Semi-Supervised Learning and SMOTE Upsampling. Journal of Advances in Information Technology, 15(4), 492–499. https://doi.org/10.12720/jait.15.4.492-499