Articles
| Open Access |
Vol. 5 No. 06 (2025): Volume 05 Issue 06
| DOI:
https://doi.org/10.37547/marketing-fmmej-05-06-02
Comparative Performance of Large Language Models for Sentiment Analysis of Consumer Feedback in the Banking Sector: Accuracy, Efficiency, and Practical Deployment
Paresh Chandra Nath , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Sajedul Karim Chy , Masters of Science in Information Technology( MSIT), Washington university of Science and Technology, USA Md Refat Hossain , Master of Business Administration (MBA), College of Business, Westcliff University, USA Md Rashel Miah , Department of Digital Communication and Media/Multimedia, Westcliff University, USA Sakib Salam Jamee , Department of Management Information Systems, University of Pittsburgh, PA, USA Mohammad Kawsur Sharif , Department of Business Administration and Management, Washington University of Virginia, USA Md Shakhaowat Hossain , Department of Management Science and Quantitative Methods, Gannon University, USA Mousumi Ahmed , Master’s in Public Administration, University of Dhaka, Dhaka, Bangladesh.Abstract
In the rapidly evolving banking sector, understanding consumer sentiment is crucial for informed decision-making and enhancing customer experiences. This study investigates the efficacy of large language models (LLMs) for sentiment analysis of consumer feedback within the banking domain. We systematically evaluate five state-of-the-art LLMs—DistilBERT, BERT-base, RoBERTa-base, GPT-3.5, and GPT-4—on a domain-specific dataset of 10,000 consumer feedback entries collected from online banking forums and customer reviews. Each model is rigorously assessed in terms of accuracy, precision, recall, F1-score, and computational cost. Our findings reveal that GPT-4 delivers the highest accuracy and performance across all evaluation metrics but requires significant computational resources, making it less feasible for real-time deployment in cost-sensitive scenarios. In contrast, RoBERTa-base and BERT-base strike a balance between accuracy and resource efficiency, while DistilBERT emerges as the most cost-effective and computationally efficient solution. These results highlight the trade-offs between performance and practical deployment considerations in real-world banking environments. The study underscores the transformative potential of LLM-driven sentiment analysis in the financial sector, offering valuable insights for banks and financial institutions aiming to leverage AI for strategic decision-making and customer satisfaction improvements.
Keywords
sentiment analysis, large language models, consumer feedback, banking sector, RoBERTa, GPT-4, cost-effective models, real-time applications, customer satisfaction, artificial intelligence.
References
Hossain, M. N., Hossain, S., Nath, A., Nath, P. C., Ayub, M. I., Hassan, M. M., ... & Rasel, M. (2024). ENHANCED BANKING FRAUD DETECTION: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS. American Research Index Library, 23-35.
Liu, C., Arulappan, A. K., Naha, R., Mahanti, A., Kamruzzaman, J., & Ra, I. H. (2023). Large language models and sentiment analysis in financial markets: A review, datasets and case study. IEEE Access.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Singh, S., & Srivastava, R. (2021). Sentiment analysis of online customer reviews in banking sector using deep learning. Journal of Banking and Financial Technology, 5(2), 115–130.
Kaur, P., Dhir, A., Singh, N., Sahu, G., & Almotairi, M. S. (2021). An innovation resistance theory perspective on mobile payment solutions. Journal of Retailing and Consumer Services, 60, 102456.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.
Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
Devlin et al., (2019). Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., … & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.
Zhang, B., Yang, H., Zhou, T., Babar, A., & Liu, X. Y. (2023). Enhancing financial sentiment analysis via retrieval augmented large language models. arXiv preprint arXiv:2310.04027.
Siddiqui, S. A., & Alam, M. (2023). Sentiment analysis of digital banking reviews using machine learning and large language models. Electronics, 14(11), 2125.
Kirtac, K., & Germano, G. (2024). Sentiment trading with large language models. arXiv preprint arXiv:2412.19245.
Nguyen, Q. G., Nguyen, L. H., Hosen, M. M., Rasel, M., Shorna, J. F., Mia, M. S., & Khan, S. I. (2025). Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction. The American Journal of Applied sciences, 7(01), 21-30.
Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. Frontline Marketing,Management and Economics Journal, 4(12), 66-83.
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Nath, F., Chowdhury, M. O. S., & Rhaman, M. M. (2023). Navigating produced water sustainability in the oil and gas sector: A Critical review of reuse challenges, treatment technologies, and prospects ahead. Water, 15(23), 4088.
PHAN, H. T. N., & AKTER, A. (2024). HYBRID MACHINE LEARNING APPROACH FOR ORAL CANCER DIAGNOSIS AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES. Universal Publication Index e-Library, 63-76.
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Nath, F., Asish, S., Debi, H. R., Chowdhury, M. O. S., Zamora, Z. J., & Muñoz, S. (2023, August). Predicting hydrocarbon production behavior in heterogeneous reservoir utilizing deep learning models. In Unconventional Resources Technology Conference, 13–15 June 2023 (pp. 506-521). Unconventional Resources Technology Conference (URTeC).
Ahmmed, M. J., Rahman, M. M., Das, A. C., Das, P., Pervin, T., Afrin, S., ... & Rahman, N. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. American Research Index Library, 31-44.
Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.
Siddique, M. T., Jamee, S. S., Sajal, A., Mou, S. N., Mahin, M. R. H., Obaid, M. O., ... & Hasan, M. (2025). Enhancing Automated Trading with Sentiment Analysis: Leveraging Large Language Models for Stock Market Predictions. The American Journal of Engineering and Technology, 7(03), 185-195.
Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19
Nguyen, A. T. P., Jewel, R. M., & Akter, A. (2025). Comparative Analysis of Machine Learning Models for Automated Skin Cancer Detection: Advancements in Diagnostic Accuracy and AI Integration. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 15-26.
Nguyen, A. T. P., Shak, M. S., & Al-Imran, M. (2024). ADVANCING EARLY SKIN CANCER DETECTION: A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR MELANOMA DIAGNOSIS USING DERMOSCOPIC IMAGES. International Journal of Medical Science and Public Health Research, 5(12), 119-133.
Phan, H. T. N., & Akter, A. (2025). Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 27-37.
Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTIC ACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.
Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.
Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
Mazharul Islam Tusher, “Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation”, ijmsphr, vol. 6, no. 05, pp. 63–72, May 2025.
Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry”., Int Bus & Eco Adv Jou, vol. 6, no. 05, pp. 90–101, May 2025, doi: 10.55640/business/volume06issue05-05.
Tamanna Pervin, Sharmin Akter, Sadia Afrin, Md Refat Hossain, MD Sajedul Karim Chy, Sadia Akter, Md Minzamul Hasan, Md Mafuzur Rahman, & Chowdhury Amin Abdullah. (2025). A Hybrid CNN-LSTM Approach for Detecting Anomalous Bank Transactions: Enhancing Financial Fraud Detection Accuracy. The American Journal of Management and Economics Innovations, 7(04), 116–123. https://doi.org/10.37547/tajmei/Volume07Issue04-15
Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19
Mazharul Islam Tusher, Han Thi Ngoc Phan, Arjina Akter, Md Rayhan Hassan Mahin, & Estak Ahmed. (2025). A Machine Learning Ensemble Approach for Early Detection of Oral Cancer: Integrating Clinical Data and Imaging Analysis in the Public Health. International Journal of Medical Science and Public Health Research, 6(04), 07–15. https://doi.org/10.37547/ijmsphr/Volume06Issue04-02
Safayet Hossain, Ashadujjaman Sajal, Sakib Salam Jamee, Sanjida Akter Tisha, Md Tarake Siddique, Md Omar Obaid, MD Sajedul Karim Chy, & Md Sayem Ul Haque. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22–33. https://doi.org/10.37547/tajet/Volume07Issue04-04
Ayub, M. I., Bhattacharjee, B., Akter, P., Uddin, M. N., Gharami, A. K., Islam, M. I., ... & Chambugong, L. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141-150.
Jamee, S. S., Sajal, A., Obaid, M. O., Uddin, M. N., Haque, M. S. U., Gharami, A. K., ... & FARHAN, M. (2025). Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry. International Interdisciplinary Business Economics Advancement Journal, 6(05), 90-101.
Siddique, M. T., Uddin, M. J., Chambugong, L., Nijhum, A. M., Uddin, M. N., Shahid, R., ... & Ahmed, M. (2025). AI-Powered Sentiment Analytics in Banking: A BERT and LSTM Perspective. International Interdisciplinary Business Economics Advancement Journal, 6(05), 135-147.
Thakur, K., Sayed, M. A., Tisha, S. A., Alam, M. K., Hasan, M. T., Shorna, J. F., ... & Ayon, E. H. (2025). Multimodal Deepfake Detection Using Transformer-Based Large Language Models: A Path Toward Secure Media and Clinical Integrity. The American Journal of Engineering and Technology, 7(05), 169-177.
Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.
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Copyright (c) 2025 Paresh Chandra Nath, Md Sajedul Karim Chy, Md Refat Hossain, Md Rashel Miah
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