Leveraging Sentiment Analysis to Optimize Customer Experience in Digital Wallets: A Case of Opay Wallet

Authors

  • Aminu Bashir Suleiman
    Department of Cyber Security, Federal University Dutsin-Ma
  • Emmy Danny Ajik
    Federal University Dutsinma
    https://orcid.org/0009-0007-0205-4837
  • Sanusi Abdul Sule
    Department of Information Technology, Federal University Dutsin-Ma

Keywords:

Sentiment Analysis, Opay Wallet, Fintech, Customer Experience, Natural Language Processing (NLP)

Abstract

With the growing adoption of digital financial services, customer experience has become a key differentiator for fintech companies. Sentiment analysis offers a data-driven approach to understanding user opinions, enabling companies to improve service quality and address customer concerns proactively. This study leverages sentiment analysis to assess user feedback on the Opay Wallet, a leading mobile payment platform. Using a dataset of user reviews collected from the Google Play Store between September 2018 and December 2024, TextBlob was employed to classify sentiments as positive, neutral, or negative. The results indicate that 82.2% of reviews were positive, 13.5% were neutral, and 5.5% were negative, suggesting an overall favorable perception of Opay Wallet. Furthermore, Latent Dirichlet Allocation (LDA) topic modeling was applied to identify key themes in user feedback. The analysis revealed that users frequently praised transaction speed, ease of use, and reliability, while negative reviews highlighted concerns about customer support and occasional transaction failures. These insights underscore the importance of optimizing fintech services based on real-time user sentiment. The study recommends continuous sentiment monitoring and AI-driven customer support enhancements to maintain user satisfaction and competitive advantage in the digital payments industry.

Dimensions

Ajayi, O., & Ojo, A. (2021). Adoption of mobile payment platforms in Nigeria: Trends and challenges. JOURNAL OF FINANCIAL INNOVATION, 12(3), 45-56. https://doi.org/10.1186/s13014-021-00331-1

Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly Media.

Blei, D. M., Ng, A. Y., & Lafferty, J. D. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Bouaddi, M., Lahriga, M., & Beddaa, M. (2024). Digital Transformation Of Moroccan Companies And Its Impact On Service Quality: A Case Of The Banking Sector. Revue Internationale du Marketing et Management Stratégique. https://doi.org/10.34874/PRSM.RIMMS-VOL6ISS2.53040

Deviani, Winda Aulia; Kusumahadi, Krishna; Nurhazizah, Eva. Service Quality For Digital Wallet In Indonesia Using Sentiment Analysis And Topic Modelling. International Journal of Business and Technology Management. https://doi.org/10.55057/ijbtm.2022.4.1.6

Effiom, L., & Edet, S. E. (2020). Financial innovation and the performance of small and medium scale enterprises in Nigeria. In Journal of Small Business & Entrepreneurship (Vol. 34, Issue 2, pp. 141–174). Informa UK Limited. https://doi.org/10.1080/08276331.2020.1779559

Harnadi, B., & Widiantoro, A. D. (2023). Evaluating the Performance and Accuracy of Supervised Learning Models on Sentiment Analysis of E-Wallet. https://doi.org/10.1109/incit60207.2023.10413111

Helmayanti, S. A., Hamami, F., & Fa'rifah, R. Y. (2023). Application of the tf-idf and naïve bayes algorithms for aspect-based sentiment analysis of flip application reviews on the google play store. Indonesian Journal: Information and Communication Management. https://doi.org/10.35870/jimik.v4i3.415

Hutto, C. J., & Gilbert, E. E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the Eighth International Conference on Weblogs and Social Media (ICWSM 2014), 216-225.

Jaluthria, K., Singh, K., & Jindal, R. (2024). Sentiment Analysis of Unified Payments Interface (UPI) Payment App Review using Machine Learning Algorithm. 1–6. https://doi.org/10.1109/incet61516.2024.10593356

Jurafsky, D., & Martin, J. H. (2020). SPEECH AND LANGUAGE PROCESSING (3rd ed.). Prentice Hall.

Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-84. https://doi.org/10.1509/jm.15.0420

Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers.

Maindola, P., Singhal, N., & Dubey, A. D. (2018). Sentiment Analysis of Digital Wallets and UPI Systems in India Post Demonetization Using IBM Watson. International Conference on Computer Communication and Informatics. https://doi.org/10.1109/ICCCI.2018.8441441

Manning, C. D., Raghavan, P., & Schütze, H. (2008). INTRODUCTION TO INFORMATION RETRIEVAL. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1-135. https://doi.org/10.1561/1500000011

Renaldi, F. (2023). Sentiment Analysis Of E-Wallets on Twitter social media With Naïve Bayes and Lexicon-Based Methods. https://doi.org/10.46254/ap03.20220200

Rusydiana, A. S., As-Salafiyah, A. (2022). Shariah Fintech. In Ekonomi Islam Indonesia (Vol. 4, Issue 2). Sharia Economic Applied Research and Training (SMART) Insight. https://doi.org/10.58968/eii.v4i2.98

Uchenna Innocent Nnaomah, Samuel Aderemi, David Olanrewaju Olutimehin, Omamode Henry Orieno, & Damilola Oluwaseun Ogundipe. (2024). Digital Banking And Financial Inclusion: A Review Of Practices In The USA and Nigeria. In Finance and Accounting Research Journal (Vol. 6, Issue 3, pp. 463–490). Fair East Publishers. https://doi.org/10.51594/farj.v6i3.971

Widianto, I. S., Ramadhan, Y. R., Ramadhan, Y. R., Komara, M. A., Komara, M. A. (2024). Gopay, Shopeepay and OVO e-wallet sentiment analysis using the Naive Bayes algorithm. JITET: Journal of Informatics and Applied Electrical Engineering, 12 (3S1). https://doi.org/10.23960/jitet.v12i3s1.5277

Zahra, S., & Alamsyah, A. (2022). Digital Wallet Service Quality Analysis using Multiclass Classification and Sentiment Analysis. 256–261. https://doi.org/10.1109/ICOIACT55506.2022.9971936

Published

2024-12-31

How to Cite

Suleiman, A. B., Ajik, E. D., & Sule, S. A. (2024). Leveraging Sentiment Analysis to Optimize Customer Experience in Digital Wallets: A Case of Opay Wallet. Nigerian Journal of Physics, 33(4), 147-156. https://doi.org/10.62292/njp.v33i4.2024.322

Issue

Vol. 33 No. 4 (2024): Nigerian Journal of Physics - Vol. 33 No. 4

Section

Articles
Copyright & Licensing

How to Cite

Suleiman, A. B., Ajik, E. D., & Sule, S. A. (2024). Leveraging Sentiment Analysis to Optimize Customer Experience in Digital Wallets: A Case of Opay Wallet. Nigerian Journal of Physics, 33(4), 147-156. https://doi.org/10.62292/njp.v33i4.2024.322