Classification of customer feedbacks using sentiment analysis towards mobile banking applications

Abstract

Innovation and technology have subsequently transformed banking industry’s way of delivering products and services to their customer. Mobile banking is an effective way of performing transaction as it can be performed anywhere and anytime. The evolution of banking experience is important to fulfil customers’ need and demand especially in highly competitive banking industry. Through mobile banking application, customer can express their satisfaction and dissatisfaction directly on the application store platform. The fulfilment of customer’s satisfaction is important to avoid customer attrition. This research focused on customer feedbacks towards six mobile banking application in Malaysia which is Maybank, Commerce International Merchant Bankers (CIMB), Public Bank, Hong Leong Bank, Rashid Hussein Bank (RHB) and AmBank. This research aims to identify keywords related to customer feedback towards mobile banking, classify the sentiment and evaluate the accuracy performance by using supervised machine learning algorithm of support vector machine (SVM) and naïve Bayes (NB). The result shows that linear SVM is the best model with the highest value in all accuracy, precision, recall, including F1-score with value 97.17%, 97.21%, 97.17% and 97.18% respectively. With this high accuracy value, this model would have better performance in analyzing the classification of customer feedback in mobile banking application.