Sentiment Monitoring of Google Maps Reviews Using Naive Bayes and Support Vector Machine at Aveta Hotel Malioboro

Rinaldi Hamzah (Student of Computer Science, Faculty of Science & Technology, Universitas Teknologi Yogyakarta)
Arif Pramudwiatmoko (Department of Enginering, Faculty of Science & Technology, Universitas Teknologi Yogyakarta)

Formosa Journal of Science and Technology (FJST)
Vol. 5 No. 6 (2026): June 2026

Manual monitoring of hotel customer reviews on Google Maps is increasingly inefficient due to growing review volumes, particularly in detecting negative sentiment. This study proposes a web-based sentiment monitoring system integrated with Naive Bayes and Support Vector Machine (SVM) for automated sentiment classification of Google Maps reviews at Aveta Hotel Malioboro. A dataset of 2,449 historical reviews was collected via DOM scraping and processed through text preprocessing and TF-IDF feature extraction. SVM with minority-class oversampling achieved the highest performance: accuracy 0.9540, precision 0.9514, recall 0.9540, and F1 Score 0.9522. The implemented system retrieves reviews periodically via SerpAPI, classifies sentiment automatically, and delivers real-time Telegram notifications, enabling structured and data-driven hotel review monitoring