Mapping the Polarity of Tourist Opinions on Indonesian Destinations through Google Maps Reviews Using Supervised Learning Methods - Sa’adah, Siti Miftahus; Umam, Khothibul; Handayani, Maya Rini; Mustofa, Mokhammad Iklil (Journal of Applied Informatics and Computing, 2025)
The advancement of information technology has transformed how individuals seek information and plan their travels, notably through online reviews of tourist attractions on platforms like Google Maps. However, these reviews do not always align with visitors' expectations, necessitating further analysis to comprehend the underlying sentiments. The objective of this research is to inspect the performance of multiple machine learning algorithms in executing sentiment analysis on user generated reviews related to tourist attractions in Indonesia. The algorithms examined include Multinomial Naïve Bayes, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Extra Trees Classifier. The research process encompasses data collection and labeling, data preprocessing, exploratory data analysis (EDA), Word Cloud visualization, feature extraction, classification implementation, and performance evaluation. Experimental results indicate that the K-Nearest Neighbors (KNN) algorithm attain the most accuracy and F1-score of 97%, indicating its effectiveness in categorizing text-based sentiment reviews sourced from the Google Maps platform.