Analyzing Public Sentiment on Digital Banks in Indonesia via Social Media X

Erman Arif (1) , Suherman Suherman (1) , Aris Puji Widodo (1)
(1) Diponegoro University, Indonesia

Abstract

This study aims to analyze public sentiment towards digital banks in Indonesia, specifically Bank Jago, using data from social media Twitter. Sentiment analysis methods were used to classify tweets into positive, negative and neutral categories. The findings show that public perceptions are generally positive, with a focus on technological innovation and ease of service. However, key complaints related to technical issues and customer service remain noteworthy. Most tweets had neutral to positive sentiments, reflecting a favorable public view of digital banks. These results highlight the importance for digital banks to continuously improve the customer service and technical stability of their apps to maintain a good reputation in the eyes of the public. In addition, the positive sentiments that arise regarding technological innovation can be leveraged to reinforce the bank's image as a modern and efficient institution. Recommendations from this study include developing a more responsive customer support system and improving app stability. With these measures, digital banks can maintain public trust and compete in an increasingly competitive market, as well as increase customer satisfaction and loyalty. This research provides valuable insights for digital bank business strategy in Indonesia.

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References

[1] N. Ahmad and T. Klotz, “Digital banking transformation and the rise of neobanks: A case study of southeast asia,” International Journal of Bank Marketing, vol. 40, no. 3, pp. 475–495, 2022.

[2] Y. Wang, M. Wei, P. Wang, Y. Gao, T. Yu, N. Meng, H. Liu, X. Zhang, K. Wang, and Q. Wu, “Insight into public sentiment and demand in china’s public health emergency response: a weibo data analysis,” BMC Public Health, vol. 25, no. 1, pp. 1–13, 2025.

[3] S. Banerjee and A. Gupta, “Exploring customer satisfaction in digital banking: A study on service quality and trust,” Journal of Retailing and Consumer Services, vol. 64, p. 102768, 2022.

[4] S. Pratama and L. A. M. Nelloh, “Leveraging influencer marketing in higher education: Key roles, sectors, platforms, and influencer types for institutional branding,” Startupreneur Business Digital (SABDA Journal), vol. 4, no. 2, pp. 134–145, 2025.

[5] G. Kaur and S. Kapoor, “Investigating digital banking transformation: Impact on customer satisfaction and trust,” Technological Forecasting and Social Change, vol. 168, p. 120726, 2021.

[6] F. Durrani, N. Ahmed, J. Zandstra, R. Wang, L. S. Lakshmanan, and S. Lin, “Sentiment search: Make the internet your focus group,” Georgia Tech Library, 2022.

[7] K. Khoirunurrofik, C. Endrina Dewi, and A. Marwah Zulkarnain, “Exploring the public sentiment of local community on major infrastructure development: Evidence from media news and twitter data,” Journal of Human Behavior in the Social Environment, vol. 34, no. 3, pp. 423–443, 2024.

[8] B. Andrian, T. Simanungkalit, I. Budi, and A. F. Wicaksono, “Sentiment analysis on customer satisfaction of digital banking in indonesia,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 3, 2022.

[9] N. Islam and H. Rahman, “Sentiment analysis on digital banking services using social media data,” Journal of Financial Data Science, vol. 4, no. 1, pp. 78–93, 2022.

[10] P. Mishra and P. Sinha, “Digital banking in emerging economies: A study on customer satisfaction and trust,” Journal of Banking & Finance, vol. 137, p. 106416, 2022.

[11] J. Wilson and E. Erika, “Empowering eco-innovation through digitalization in startup enterprises,” Startupreneur Business Digital (SABDA Journal), vol. 4, no. 2, pp. 146–154, 2025.

[12] G. Hristova and N. Netov, “Analysis of public sentiments and emotions in the government domain,” Industry 4.0, vol. 8, no. 1, pp. 32–35, 2023.

[13] Y. Liu and H. Zhang, “Sentiment analysis of banking services using twitter data: A case study of neobanks in asia,” Journal of Big Data Analytics in Finance, vol. 5, no. 1, pp. 98–114, 2022.

[14] C. Lukita, M. Hardini, S. Pranata, D. Julianingsih, and N. P. L. Santoso, “Transformation of entrepreneurship and digital technology students in the era of revolution 4.0,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 3, pp. 291–304, 2023.

[15] G. B. Ferilli, E. Palmieri, S. Miani, and V. Stefanelli, “The impact of fintech innovation on digital financial literacy in europe: Insights from the banking industry,” Research in International Business and Finance, vol. 69, p. 102218, 2024.

[16] L. M. Gandy, L. V. Ivanitskaya, L. L. Bacon, and R. Bizri-Baryak, “Public health discussions on social media: evaluating automated sentiment analysis methods,” JMIR Formative Research, vol. 9, no. 1, p. e57395, 2025.

[17] A. C. Pramono and W. Prahiawan, “Effect of training on employee performance with competence and commitment as intervening,” Aptisi Transactions on Management, vol. 6, no. 2, pp. 142–150, 2022.

[18] N. Torwane, R. Lalloo, D. Ha, and L. Do, “Mapping the “x” debate: Water fluoridation sentiment analysis with advanced machine learning,” Journal of Public Health Dentistry, 2025.

[19] P. Dangaiso, P. Mukucha, F. Makudza, T. Towo, K. Jonasi, and D. C. Jaravaza, “Examining the interplay of internet banking service quality, e-satisfaction, e-word of mouth and e-retention: A post pandemic customer perspective,” Cogent Social Sciences, vol. 10, no. 1, p. 2296590, 2024.

[20] Z. Quan, T. Sun, M. Su, and J. Wei, “Multimodal sentiment analysis based on cross-modal attention and gated cyclic hierarchical fusion networks,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, p. 4767437, 2022.

[21] Q. Aini, P. Purwanti, R. N. Muti, E. Fletcher et al., “Developing sustainable technology through ethical ai governance models in business environments,” ADI Journal on Recent Innovation, vol. 6, no. 2, pp. 145–156, 2025.

[22] S. Choudhury, J. Paul, and D. Bhattacharjee, “Digital banking and customer experience: A review of research and future directions,” Journal of Business Research, vol. 144, pp. 18–31, 2022.

[23] A. Hermawan, W. Sunaryo, and S. Hardhienata, “Optimal solution for ocb improvement through strengthening of servant leadership, creativity, and empowerment,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 1Sp, pp. 11–21, 2023.

[24] M. Nawaz and R. Hassan, “Understanding the influence of digital banking on customer loyalty in pakistan,” Asian Journal of Business and Accounting, vol. 14, no. 1, pp. 45–65, 2021.

[25] S. Zhao, L. Chen, Y. Liu, M. Yu, and H. Han, “Deriving anti-epidemic policy from public sentiment: A framework based on text analysis with microblog data,” Plos one, vol. 17, no. 8, p. e0270953, 2022.

[26] S. Yu, S. He, Z. Cai, I. Lee, M. Naseriparsa, and F. Xia, “Exploring public sentiment during covid-19: A cross country analysis,” IEEE Transactions on Computational Social Systems, vol. 10, no. 3, pp. 1083–1094, 2022.

[27] S. R. Putri, M. Arifin, and S. Supriyono, “Public sentiment analysis of nadiem makarim as minister of education, culture, research, and technology using support vector machine (svm),” SISTEMASI, vol. 14, no. 2, pp. 826–834, 2025.

[28] H. Purnama and A. Setyawan, “Digital banking satisfaction and trust: A comparative study in southeast asia,” Asia-Pacific Journal of Financial Studies, vol. 51, no. 4, pp. 512–528, 2022.

[29] K. Katta, “Analyzing user perceptions of large language models (llms) on reddit: Sentiment and topic modeling of chatgpt and deepseek discussions,” arXiv preprint arXiv:2502.18513, 2025.

[30] S. Y. Putri, L. Meria et al., “Pengaruh persepsi nilai dan kepercayaan terhadap keputusan pembelian yang di mediasi oleh minat beli,” Technomedia Journal, vol. 8, no. 1, pp. 92–107, 2023.

[31] T. Marques, S. Cez´ario, J. Lacerda, R. Pinto, L. Silva, O. Santana, A. G. Ribeiro, A. S. Cruz, A. E. Miranda, A. Cadaxa et al., “Sentiment analysis in understanding the potential of online news in the public health crisis response,” International journal of environmental research and public health, vol. 19, no. 24, p. 16801, 2022.

[32] U. Yaqub, S. A. Chun, V. Atluri, and J. Vaidya, “Analyzing social media messages of public sector organizations utilizing sentiment analysis and topic modeling,” Information Polity, vol. 26, no. 4, pp. 375–390, 2021.

[33] K. K. Bhagat, S. Mishra, A. K. Parida, A. Samal, G. Lampropoulos, and A. Dixit, “Analyzing the discourse on open educational resources on twitter: a sentiment analysis approach,” Educational technology research and development, pp. 1–24, 2025.

[34] R. N. Mauliza and Y. R. Sipayung, “Penerapan text mining dalam menganalisis pendapat masyarakat terhadap pemilu 2024 pada media sosial x menggunakan metode naive bayes,” vol, vol. 9, pp. 1–16, 2024.

[35] S. N. Khofiyah and P. Subarkah, “Comparison of naive bayes and svm in public opinion sentiment analysis on platform x,” Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM), pp. 125–138, 2025.

[36] R. A. Sunarjo, M. H. R. Chakim, S. Maulana, and G. Fitriani, “Management of educational institutions through information systems for enhanced efficiency and decision-making,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 47–61, 2024.

[37] E. Safitri, W. A. Syukrilla, and I. N. L. Fitriana, “Logistic regression for sentiment analysis of insecurity phenomena on platform x,” J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, vol. 18, no. 1, pp. 948–956, 2025.

[38] S. Jabalameli, Y. Xu, and S. Shetty, “Spatial and sentiment analysis of public opinion toward covid-19 pandemic using twitter data: At the early stage of vaccination,” International Journal of Disaster Risk Reduction, vol. 80, p. 103204, 2022.

[39] S. Gupta and S. Chatterjee, “Public sentiment toward digital financial services: An empirical investigation,” Information Systems Frontiers, vol. 24, no. 3, pp. 629–645, 2022.

[40] M. O. Ibrohim, C. Bosco, and V. Basile, “Sentiment analysis for the natural environment: A systematic review,” ACM Computing Surveys, vol. 56, no. 4, pp. 1–37, 2023.

[41] A. Albladi, M. Islam, and C. Seals, “Sentiment analysis of twitter data using nlp models: a comprehensive review,” IEEE Access, 2025.

[42] N. Lutfiani, D. A. Astrieta, V. Wildan, H. Sulistyaningrum, M. R. Anwar, and E. D. Astuti, “Emotional well-being and psychological support in infertility a multi-modal ai approach,” International Journal of Cyber and IT Service Management, vol. 5, no. 1, pp. 81–92, 2025.

[43] O. H. Kwon, K. Vu, N. Bhargava, M. I. Radaideh, J. Cooper, V. Joynt, and M. I. Radaideh, “Sentiment analysis of the united states public support of nuclear power on social media using large language models,” Renewable and Sustainable Energy Reviews, vol. 200, p. 114570, 2024.

[44] S. Arjun, E. Bhuvaneswari, J. Balachandar, T. Gomathi, and R. Surendran, “Predicting stock market trends using sentiment analysis on news and social media,” in 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025, pp. 1312–1317.

[45] N. P. Kumar, K. Srinivasan, and D. Ramesh, “Analyzing public sentiment towards llm: A twitter-based sentiment analysis,” in 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM). IEEE, 2023, pp. 1–8.

[46] M. Sohi, M. Pitesky, and J. Gendreau, “Analyzing public sentiment toward gmos via social media between 2019-2021,” GM Crops & Food, vol. 14, no. 1, pp. 1–9, 2023.

[47] A. Krishnan and V. Anoop, “Climatenlp: Analyzing public sentiment towards climate change using natural language processing,” arXiv preprint arXiv:2310.08099, 2023.

[48] A. Alotaibi, F. Nadeem, and M. Hamdy, “Weakly supervised deep learning for arabic tweet sentiment analysis on education reforms: Leveraging pre-trained models and llms with snorkel,” IEEE Access, 2025.

[49] M. Barari and M. Eisend, “Computational content analysis in advertising research,” Journal of Advertising, vol. 53, no. 5, pp. 681–699, 2024.

[50] L. K. Kumar, V. N. Thatha, P. Udayaraju, D. Siri, G. U. Kiran, B. Jagadesh, and R. Vatambeti, “Analyzing public sentiment on the amazon website: a gsk-based double path transformer network approach for sentiment analysis,” IEEE Access, vol. 12, pp. 28 972–28 987, 2024.

[51] T. Anderson, S. Sarkar, and R. Kelley, “Analyzing public sentiment on sustainability: A comprehensive review and application of sentiment analysis techniques,” Natural Language Processing Journal, vol. 8, p. 100097, 2024.

[52] B. Menaouer, S. Fairouz, M. B. Meriem, S. Mohammed, and M. Nada, “A sentiment analysis of the ukraine-russia war tweets using knowledge graph convolutional networks,” International Journal of Information Technology, pp. 1–18, 2025.

Authors

Erman Arif
[email protected] (Primary Contact)
Suherman Suherman
Aris Puji Widodo
Arif, E., Suherman, S., & Widodo, A. P. (2026). Analyzing Public Sentiment on Digital Banks in Indonesia via Social Media X. Aptisi Transactions on Technopreneurship (ATT), 8(1), 253–267. https://doi.org/10.34306/att.v8i1.530

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