Sentiment Analysis on Service Quality of an Online Healthcare Mobile Platform Using VADER and Roberta Pretrained Model

The development of mobile technology has penetrated into the health sector. With the development of this technology, information about health can improve the quality of health independently. The integration of information technology into the healthcare sector, namely the advancement of mobile-based health services, has significantly revolutionised the accessibility of healthcare across various regions in Indonesia. Halodoc is the leading digital health company in Indonesia and has substantially changed the axis of health services in Indonesia by providing health information that is easy to understand, credible, and accessible to everyone. This research will analyze Halodoc’s service quality based on customer reviews from Google Play Store, which will be analyzed using sentiment analysis. Specifically, this research uses VADER (Valence Aware Dictionary and Sentiment Reasoner) and Roberta Pretrained Model to analyze the sentiment of user reviews on Halodoc mobile application. The results of the sentiment analysis can provide insight into users’ overall satisfaction with the platform’s service quality. Results show that 84% users leave reviews with Neutral Sentiment Analysis, 13% with positive comments, and the smallest number of Sentiment Analysis is negative with 3%, in this application. We create correlation matrix to see correlation coefficients between sets of variables and the wordcloud. This research contributes to the growing body of research on sentiment analysis in the healthcare industry and can inform the development of strategies to improve the quality of online healthcare services.