In recent years, the banking industry has witnessed a surge in digital feedback channels, where customers regularly share their experiences and opinions. Extracting meaningful insights from this unstructured data is vital for enhancing customer satisfaction and service quality. This study presents a comparative analysis of two advanced deep learning models—Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT)—to classify the sentiment of bank customer reviews into positive, negative, and neutral categories. A cleaned and preprocessed dataset consisting of real-world customer reviews was used for model training and evaluation. The LSTM model was able to capture sequential patterns effectively, achieving competitive results in sentiment prediction. However, BERT outperformed LSTM across all evaluation metrics, achieving higher accuracy, precision, recall, and F1-score. Detailed confusion matrix analysis further confirmed BERT’s superiority in handling ambiguous and context-rich sentiment expressions. The findings highlight the practical implications of using transformer-based models in financial text analytics and provide a reliable framework for future sentiment analysis applications in the banking sector.

Sentiment Analysis, BERT, LSTM, Bank Customer Reviews, Deep Learning, Natural Language Processing, Transformer Models

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, 30, 5998–6008.

Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5-6), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 4171–4186). https://doi.org/10.48550/arXiv.1810.04805

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

Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016). Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 606–615).

Sun, C., Huang, L., & Qiu, X. (2019). Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 380–385). https://doi.org/10.48550/arXiv.1903.09588

Srivastava, A., & Dhawan, R. (2021). Financial sentiment analysis using LSTM neural network. Procedia Computer Science, 185, 189–196. https://doi.org/10.1016/j.procs.2021.05.020

Yu, J., Wang, Y., Lai, K. K., & Xu, Z. (2020). Customer complaint classification with hybrid BERT and CNN models. Expert Systems with Applications, 160, 113731. https://doi.org/10.1016/j.eswa.2020.113731

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.

Rishad, S. S. I., Shakil, F., Tisha, S. A., Afrin, S., Hassan, M. M., Choudhury, M. Z. M. E., & Rahman, N. (2025). LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS. American Research Index Library, 6-25.

Uddin, A., Pabel, M. A. H., Alam, M. I., KAMRUZZAMAN, F., Haque, M. S. U., Hosen, M. M., ... & Ghosh, S. K. (2025). Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques. The American Journal of Management and Economics Innovations, 7(01), 5-20.

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.

Al-Imran, M., Ayon, E. H., Islam, M. R., Mahmud, F., Akter, S., Alam, M. K., ... & Aziz, M. M. (2024). TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS. The American Journal of Engineering and Technology, 6(11), 20-32.

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