Sentiment Analysis of Yamet Clinic Health Services Instagram Comments With Naive Bayes

Abstract

The rapid advancement of information technology has driven the healthcare sector to adopt digital platforms in order to enhance service efficiency and accessibility. Yamet Clinic Palembang, as a therapy center for children with special needs, has utilized social media as its primary communication channel, although its official website has not yet been fully optimized. The main issue addressed in this study is the lack of understanding regarding public perception of the clinic’s digital services. Therefore, this research aims to analyze public sentiment toward Yamet Clinic Palembang’s services through comments posted on its official Instagram account. The Naïve Bayes algorithm was selected due to its strong performance in text classification tasks involving limited datasets and low computational complexity. The research process includes text preprocessing, feature weighting using Term Frequency–Inverse Document Frequency (TF-IDF), and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to balance data distribution among sentiment categories. The experimental results indicate that the developed model successfully classified public opinions into three categories—positive, neutral, and negative—with an accuracy rate of 70%. The combination of TF-IDF, Naïve Bayes, and SMOTE proved effective in capturing public perceptions of digital health services. Practically, the findings provide valuable insights for Yamet Clinic Palembang in developing a more adaptive digital communication strategy and theoretically contribute to the advancement of sentiment analysis research within the healthcare service context in Indonesia.