Sentiment Analysis on Customer Satisfaction of Digital Banking in Indonesia

Abstract

Southeast Asia, including Indonesia, is seeing an increase in digital banking adoption, owing to changing customer expectations and increasing digital penetration. The pandemic Covid-19 has hastened this tendency for digital transformation. However, customer satisfaction should not be left unmanaged during this transition. This research aims to obtain customer satisfaction of digital banking in Indonesia based on sentiment analysis from Twitter. Data collected were related to three digital banks in Indonesia, namely Jenius, Jago, and Blu. Total of 34,605 tweets were collected and analyzed within the period of August 1st 2021 to October 31st 2021. Sentiment analysis was conducted using nine standalone classifiers, Naïve Bayes, Logistic Regression, K-Nearest Neighbours, Support Vector Machines, Random Forest, Decision Tree, Adaptive Boosting, eXtreme Gradient Boosting and Light Gradient Boosting Machine. Two ensemble methods were also used for this research, hard voting and soft voting. The results of this study show that SVM among other stand-alone classifiers has the best performance when used to predict sentiments with value for F1-score 73.34%. Ensemble method performed better than using stand-alone classifier, and soft voting with 5-best classifiers performed best overall with value for F1-score 74.89%. The results also show that Jago sentiments were mainly positive, Jenius sentiments mostly were negative and for Blu, most sentiments were neutral