Aspect-Based Sentiment Analysis in Bromo Tengger Semeru National Park Indonesia Based on Google Maps User Reviews

Abstract

Technology can influence and shape a person's behavior patterns when planning tours, traveling, and after traveling. Visitors' reviews can be used as evaluation material to improve the quality of tourist destinations and become a determining factor for other tourists to visit or revisit the destinations. The process of utilizing these reviews can be done by assessing the aspects of tourist destinations based on reviews from visitors. This study aims to conduct an aspect-based sentiment analysis on one of the tourist destinations in Indonesia, namely Bromo Tengger Semeru National Park, based on reviews of Google Maps users. The aspects consist of attractions, facilities, access, and price. The sentiment classification model used is a machine learning model consisting of SVM, Complement Naïve Bayes, Logistic Regression, and transfer learning from pre-trained BERT, IndoBERT, and mBERT. Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 91.48% and 71.56%, respectively. In addition, among the machine learning models used, the SVM model gives the best results with an accuracy of 89.16% and an F1-Score of 62.23%.