Analysis of Banking Application Reviews Using a Topic-based Sentiment Analysis Approach with Rule-based Lexicon and LDA
DOI:
https://doi.org/10.59395/ijadis.v7i2.1548Keywords:
Sentiment Analysis , Banking Application, Topic Modeling , Latent Dirichlet Allocation , Lexicon-BasedAbstract
This study analyzed banking user reviews using the Livin' by Mandiri app as a case study to understand user perceptions of digital banking services. A topic-based sentiment analysis approach was implemented by integrating a rule-based lexicon method and Latent Dirichlet Allocation (LDA). Text preparation steps such as cleaning, tokenization, stopword removal, and stemming were applied to 13,539 Google Play Store reviews that were gathered between January and June 2025. Sentiment labeling using the INSET lexicon indicated that 58.7% of reviews were negative, while 41.3% were positive. Topic modeling identified five main themes representing key service aspects, with authentication issues, such as login and facial verification failures, emerging as the dominant topic in negative reviews and achieving the highest coherence score of 0.486909. Model evaluation was conducted using coherence measurement and manual validation to ensure interpretative consistency. The findings indicated that authentication system stability significantly influenced negative user perceptions, whereas transaction efficiency and ease of use contributed to positive evaluations. This approach provided interpretable insights to support data-driven service improvement in digital banking applications.
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[1] A. Ridwan, Tren Volume Transaksi Mobile Banking di Indonesia Juni 2024-Juni 2025, Databoks. [Online]. Available: https://databoks.katadata.co.id/keuangan/statistik/68edab084b7db/tren-volume-transaksi-mobile-banking-di-indonesia-juni-2024-juni-2025
[2] A. I. Tanggraeni and M. N. N. Sitokdana, Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Nave Bayes, JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785795, 2022, doi: 10.35957/jatisi.v9i2.1835. DOI: https://doi.org/10.35957/jatisi.v9i2.1835
[3] D. Mittal and S. R. Agrawal, Determining Banking Service Attributes From Online Reviews: Text Mining and Sentiment Analysis, Int. J. Bank Mark., vol. 40, no. 3, pp. 558577, 2022, doi: 10.1108/IJBM-08-2021-0380. DOI: https://doi.org/10.1108/IJBM-08-2021-0380
[4] Y. K. Oh and J. M. Kim, What Improves Customer Satisfaction in Mobile Banking Apps? An Application of Text Mining Analysis, Asia Mark. J., vol. 23, no. 4, pp. 2837, 2022, doi: 10.53728/2765-6500.1581. DOI: https://doi.org/10.53728/2765-6500.1581
[5] N. A. Rahman, S. D. Idrus, and N. L. Adam, Classification of Customer Feedbacks Using Sentiment Analysis Towards Mobile Banking Applications, IAES Int. J. Artif. Intell., vol. 11, no. 4, pp. 15791587, 2022, doi: 10.11591/ijai.v11.i4.pp1579-1587. DOI: https://doi.org/10.11591/ijai.v11.i4.pp1579-1587
[6] H. Zhao, M. Yang, X. Bai, and H. Liu, A Survey on Multimodal Aspect-Based Sentiment Analysis, IEEE Access, vol. 12, pp. 1203912052, 2024, doi: 10.1109/ACCESS.2024.3354844. DOI: https://doi.org/10.1109/ACCESS.2024.3354844
[7] A. S. R. Rufaida, A. E. Permanasari, and N. A. Setiawan, Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review, in Proceedings of the 5th International Conference on Applied Engineering (ICAE 2022), Batam, Indonesia, 2022. doi: 10.4108/eai.5-10-2022.2327474. DOI: https://doi.org/10.4108/eai.5-10-2022.2327474
[8] A. Alamsyah, W. Rizkika, D. D. A. Nugroho, F. Renaldi, and S. Saadah, Dynamic Large Scale Data on Twitter using Sentiment Analysis and Topic Modeling, 6th Int. Conf. Inf. Commun. Technol. ICoICT 2018, 2018. DOI: https://doi.org/10.1109/ICoICT.2018.8528776
[9] R. J. Dhanal and V. R. Ghorpade, Aspect Term Extraction from Multi-Source Domain Using Enhanced Latent Dirichlet Allocation, Indones. J. Electr. Eng. Comput. Sci., vol. 35, no. 1, pp. 475484, 2024, doi: 10.11591/ijeecs.v35.i1.pp475-484. DOI: https://doi.org/10.11591/ijeecs.v35.i1.pp475-484
[10] T. Ali, B. Omar, and K. Soulaimane, Analyzing Tourism Reviews Using an LDA Topic-Based Sentiment Analysis Approach, MethodsX, vol. 9, p. 101894, 2022, doi: 10.1016/j.mex.2022.101894. DOI: https://doi.org/10.1016/j.mex.2022.101894
[11] E. Edwina and T. Mauritsius, Data-Driven Insights for Mobile Banking App Improvement: A Sentiment Analysis and Topic Modelling Approach for SimobiPlus User Reviews, Int. J. Eng. Trends Technol., vol. 72, no. 6, pp. 347360, 2024, doi: 10.14445/22315381/IJETT-V72I6P132. DOI: https://doi.org/10.14445/22315381/IJETT-V72I6P132
[12] P. C. Caylak et al., Analysing Online Reviews Consumers Experiences of Mobile Travel Applications with Sentiment Analysis and Topic Modelling: The Example of Booking and Expedia, Appl. Sci., vol. 14, no. 24, p. 11800, 2024, doi: 10.3390/app142411800. DOI: https://doi.org/10.3390/app142411800
[13] I. R. Hidayat and W. Maharani, General Depression Detection Analysis Using IndoBERT Method, Intl. J. ICT, vol. 8, no. 1, pp. 4151, 2022, doi: 10.21108/ijoict.v8i1.634. DOI: https://doi.org/10.21108/ijoict.v8i1.634
[14] P. D. Adjei, C. O. Antwi, K. Adjei, and B. Zhang, Predicting Determinants Influencing User Satisfaction with Mental Health App: An Explainable Machine Learning Approach Based on Unstructured Data, Expert Syst. Appl., vol. 249, no. 2, 2024, doi: 10.1016/j.eswa.2024.123647. DOI: https://doi.org/10.1016/j.eswa.2024.123647
[15] A. G. T. AbuRaed, E. A. Prikryl, G. Carenini, and N. Z. Janjua, Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data, J. Med. Internet Res., vol. 26, pp. 118, 2024, doi: 10.2196/59425. DOI: https://doi.org/10.2196/59425
[16] A. Suharman and M. K. Sulaeman, Analisis Sentimen Pengguna Aplikasi Livin by Mandiri Menggunakan Metode Support Vector Machine ( SVM ) dengan Ekstraksi Fitur TF-IDF dan Word2Vec User Sentiment Analysis of the Livin by Mandiri Application Using the Support Vector Machine ( SVM ) Meth, J. Pendidik. dan Teknol. Indones., vol. 5, no. 8, pp. 22012212, 2025. DOI: https://doi.org/10.52436/1.jpti.941
[17] T. Ali, B. Omar, and K. Soulaimane, MethodsX Analyzing tourism reviews using an LDA topic-based sentiment analysis approach , MethodsX, vol. 9, no. November, p. 101894, 2022, doi: 10.1016/j.mex.2022.101894. DOI: https://doi.org/10.1016/j.mex.2022.101894
[18] Y. Kustyaningsih and Y. Permana, Penggunaan Latent Dirichlet Allocation ( LDA ) dan Support- Vector Machine ( SVM ) Untuk Menganalisis Sentimen Berdasarkan Aspek Dalam Ulasan Aplikasi EdLink The Use of Latent Dirichlet Allocation ( LDA ) and Support-Vector Machine ( SVM ) to Analyze Sent, TEKNIKA, vol. 13, no. 1, pp. 127136, 2024, doi: 10.34148/teknika.v13i1.746. DOI: https://doi.org/10.34148/teknika.v13i1.746
[19] N. N. Dewi, sSekar G. A. Utami, S. A. Adiar, and H. D. Cahyono, A sentiment Analysis on Skewed Product Reviews: Ben & Jerry s Ice Cream, Indones. J. Electr. Eng. Comput. Sci., vol. 39, no. 1, pp. 364373, 2025, doi: 10.11591/ijeecs.v39.i1.pp364-373. DOI: https://doi.org/10.11591/ijeecs.v39.i1.pp364-373
[20] J. P. Haumahu, S. D. H. Permana, and Y. Yaddarabullah, Fake News Classification for Indonesian News Using Extreme Gradient Boosting (XGBoost), IOP Conf. Ser. Mater. Sci. Eng., vol. 1098, no. 5, p. 052081, 2021, doi: 10.1088/1757-899x/1098/5/052081. DOI: https://doi.org/10.1088/1757-899X/1098/5/052081
[21] NLTK, Natural Language Toolkit, https://www.nltk.org/.
[22] Y. Fauziah, B. Yuwono, and A. S. Aribowo, Lexicon Based Sentiment Analysis in Indonesia Languages: A Systematic Literature Review, RSF Conf. Ser. Eng. Technol., vol. 1, no. 1, pp. 364367, 2021, doi: 10.31098/cset.v1i1.397. DOI: https://doi.org/10.31098/cset.v1i1.397
[23] Y. Sahria and D. H. Fudholi, Analisis Topik Penelitian Kesehatan di Indonesia Menggunakan Metode Topic Modeling LDA (Latent Dirichlet Allocation), J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 2, pp. 336344, 2020, doi: 10.29207/resti.v4i2.1821. DOI: https://doi.org/10.29207/resti.v4i2.1821
[24] B. Liu, Sentiment Analysis and Opinion Mining. Springer Nature, 2022.
[25] K. Mahmutovic, Analyzing User-Generated Reviews to Identify Experience Dimensions and Their Impact on Satisfaction with Mobile Banking Applications, Ekon. Vjesn., vol. 38, no. 2, pp. 283297, 2025, doi: 10.51680/ev.38.2.6. DOI: https://doi.org/10.51680/ev.38.2.6
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