Abstract
Banking has become an integral part of our lives. Fintech (Financial Technology) skyrocketed the number of people willing to use Mobile Financial Services (MFS) for their daily financial transactions. The banks are providing their services via mobile applications, which can be found on the Google Play Store. These Mobile Financial Services (MFS) provide mobility and increase efficiency by 10-fold. With an astonishing number of users came an abundant number of reviews for these apps. User reviews are the backbone of an application’s success. They provide information about hands-on experience. This study mainly focuses on the reactions of the users of such apps. Sentiment analysis is being used to draw out emotions from the users based on their written reviews. The primary goal of this paper is to examine the points of view of such application users. A total of 5414 pieces of data were collected from the Google Play Store and classified as negative, neutral, or positive. The data model has been evaluated using CNN, LSTM, and BiLSTM algorithms. Compared to CNN and LSTM, the BiLSTM algorithm produced the best model with an accuracy of 97.07%.
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Al Ryan, A., Mahmud, M., Mahi, H.H.C., Hossen, M.S., Shimul, N.I., Noori, S.R.H. (2023). FinTech: Deep Learning-Based Sentiment Classification of User Reviews from Various Bangladeshi Mobile Financial Services. In: Chandran K R, S., N, S., A, B., Hamead H, S. (eds) Computational Intelligence in Data Science. ICCIDS 2023. IFIP Advances in Information and Communication Technology, vol 673. Springer, Cham. https://doi.org/10.1007/978-3-031-38296-3_10
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