Sentiment Analysis on Mobile Banking Application Using Naive Bayes Classifer and Association Methods - Adawiyah, Rabiatul; Nugraha, Jaka (International Journal of Engineering and Technology, 2018)
The internet has grown rapidly and become the needs of the community in doing activity in various fields. One of them is the financial sector or bank.  Banks as one of the areas that are close to the community must be able to provide customer satisfaction in providing quality services. The implementation of electronic banking services (e-banking)-quality is one of the keys to the banks to gather customers’ funds. One of the e-banking services is the mobile banking is used exclusively in the cell phones to the efficiency of the customer in doing transaction.  In order to view the customer response to the performance of the mobile banking facility, review  to the client according to the application for six months was investigasted. The Data reviews was taken from the Google Play. The review was analyzed using Sentiment analysis which is the process for classifying opinions into the category of positive or negative signals. This classification is then analyzed by using text mining with the Association of the words. The result are an important and useful information for the company. The method used in this classification is Naïve Bayes Classifer (NBC). The level of accuracy using the NBC is 89.41%. The accuracy showed that the classification by the system has been good.