Sentiment Analysis and Emotional Reviews of Hospital Services Using Naïve Bayes and Support Vector Machine (SVM)

Hospitals are crucial healthcare institutions that play a vital role in providing medical services to the community. Patient perceptions and experiences regarding hospital services are often reflected in reviews left on digital platforms such as Google Maps. This study aims to analyze public sentiment and emotions toward hospital services in Semarang City using Google Maps user reviews. A total of 16,364 reviews from 21 hospitals were collected between 2023 and 2024. Sentiment labelling was performed using a lexicon-based approach to classify reviews into positive, negative, and neutral categories. To explore the emotions expressed in the reviews, the NRC Emotion Lexicon (EmoLex) was used to identify eight basic emotions. The analysis revealed that 'Trust' was the most dominant emotion (7,090 words), indicating high patient confidence, followed by 'Joy'. Furthermore, for predictive modelling using Naïve Bayes and Support Vector Machine (SVM) algorithms, SVM achieved 90% accuracy, whereas Naïve Bayes achieved only 78%. The results of this analysis are expected to serve as input for hospitals to improve service quality and as a reference for prospective patients in selecting a hospital.