Sentiment Analysis of User Reviews on BSI Mobile and Action Mobile Applications on the Google Play Store Using Multinomial Naive Bayes Algorithm

Mobile banking services are designed to facilitate customer transactions. Bank Syariah Indonesia (BSI) and Bank Aceh also provide these online services through their respective applications, BSI Mobile and Action Mobile. The mobile banking apps aim to simplify customer transactions, which can be conducted remotely via several features, from transfers, payments, and purchases to zakat payments, by simply connecting to the internet. Therefore, this research aims to classify the sentiment of user reviews for BSI Mobile and Action Mobile applications on Google Play Store to understand the users' experiences. The Multinomial Naïve Bayes algorithm is used in this study, where the algorithm analyzes and classifies the user reviews into positive and negative sentiment categories. The study involves several stages, such as text preprocessing, sentiment visualization, splitting the data into an 80:20 ratio for training and testing datasets, and training the model using the Multinomial Naïve Bayes algorithm. The results of this study show that the Multinomial Naïve Bayes algorithm performs well in analyzing user sentiment for BSI Mobile and Action Mobile, achieving an accuracy of 78.7%, precision of 76.5%, recall of 86.2%, and an F1-score of 80.6% for BSI Mobile, and an accuracy of 85.6%, precision of 75%, recall of 75%, and an F1-score of 75% for Action Mobile. Additionally, the sentiment classification results reveal that 52.8% of BSI Mobile user reviews are positive and 47.2% are negative, while for Action Mobile, 35.1% are positive and 64.9% are negative. For BSI Mobile, 21,497 reviews express a positive sentiment with dominant keywords such as "updated," "good," "balance," "transaction," and "thank." Meanwhile, for Action Mobile, 274 reviews express a negative sentiment with dominant keywords such as "transaction," "application," "network," "register," "please," and "update."