Using machine learning to improve a telco self-service mobile application in Indonesia

Abstract

The use of mobile applications extends to the telecommunication sector, mainly due to COVID-19. Failure to provide it can cause dissatisfaction and result in the removal of the mobile application. Moreover, this leads to lost service opportunities, so paying attention to the mobile application's quality is essential. There has yet to be a study on measuring the service quality of a self-service mobile application in the telecommunication sector using online customer reviews. This study uses sentiment analysis and topic modeling to determine the service quality of a self-service mobile application in the telecommunication sector from reviews on Google Play Store and Apple App Store. This study uses myIndiHome as a case study. The total data obtained from both platforms are 20,452 reviews. Sentiment analysis was performed using Naïve Bayes, support vector machine, and logistic regression, while topic modeling was performed using latent dirichlet allocation. The results show that logistic regression performs better than support vector machine and Naïve Bayes. Meanwhile, topic modeling shows that the positive review data has three topics, including application features, products/services, and application interfaces. Moreover, the negative review data has five topics, including application availability, application feature reliability, application processing speed, bugs, and application reliability.