A Comparative Analysis of User Sentiment and Review Patterns on The Dana and Seabank Apps Using The Naive Bayes, Support Vector Machine and Logistic Regression Algorithms

Abstract

The development of financial technology (fintech) in Indonesia has driven a significant increase in the use of digital financial applications, particularly e-wallets and digital banks. The DANA application as a representative of e-wallet services and SeaBank as a representative of digital banking services have attracted millions of users with different service characteristics. These differences in service characteristics potentially generate different patterns of user sentiment and reviews. This study aims to analyze and compare user sentiment and review patterns of the DANA and SeaBank applications using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. User review data were collected from the Google Play Store and processed through text preprocessing stages including case folding, tokenization, stopword removal, and stemming. Text features were extracted using the TF-IDF method before being classified into positive, negative, and neutral sentiment categories. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The comparative analysis results are expected to provide a comprehensive overview of differences in user sentiment based on the type of fintech service, while also identifying the most optimal machine learning algorithm for sentiment analysis of digital financial application reviews.