Sentiment Classification of Riliv Application Users Using the Support Vector Machine Algorithm

Abstract

Riliv is a digital mental health application that provides online counseling, guided meditation, and mindfulness training services. The increasing public awareness of mental health has led to a significant growth in the number of Riliv users. However, user reviews on the Google Play Store show varied opinions, both positive and negative. This study aims to classify user sentiment toward the Riliv application based on user reviews. Data were collected using web scraping techniques and processed through text preprocessing stages, feature extraction using Term Frequency Inverse Document Frequency, and classification using the Support Vector Machine algorithm. The results show that the proposed model achieved an accuracy of 83.3 percent, with sentiment distribution consisting of 58 percent positive, 32 percent negative, and 10 percent neutral. Negative sentiments are mainly related to technical issues and slow service response. The results of this study can be used as evaluation material to improve the quality of digital mental health services.