Aspect-Based Sentiment Analysis of Borobudur Temple Reviews Use Support Vector Machine Algorithm

An analysis of Borobudur Temple reviews reveals that accessibility and visual image are the most critical aspects needing improvement to enhance visitor satisfaction. Using Support Vector Machine (SVM) algorithm, researchers identified these areas as having the highest concentration of negative sentiment, which could significantly impact tourism growth. The study highlights the importance of aspect-based sentiment analysis in managing tourist attractions effectively. However, the model's accuracy was limited to 0.8148 due to imbalanced data distribution across aspects and reviews.

Yudianto, M. R. A., Sukmasetya, P., Hasani, R. A., & Maimunah. (2024). Aspect-Based Sentiment Analysis of Borobudur Temple Reviews Use Support Vector Machine Algorithm. E3S Web of Conferences, 500, 1005. https://doi.org/10.1051/e3sconf/202450001005