Classification of customer feedbacks using sentiment analysis towards mobile banking applications

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