Articles
| Open Access |
DOI: A COMPREHENSIVE STUDY OF MACHINE LEARNING APPROACHES FOR CUSTOMER SENTIMENT ANALYSIS IN BANKING SECTOR
Salma Akter , Department of Public Administration, Gannon University, Erie, PA, USA Fuad Mahmud , Department of Information Assurance and Cybersecurity, Gannon University, USA Tauhedur Rahman , Dahlkemper School of Business, Gannon University, USA Md Jamil Ahmmed , Department of Information Technology Project Management, Business Analytics, St. Francis College, USA Md Kafil Uddin , Dahlkemper School of Business, Gannon University, USA Md Imdadul Alam , Master of Science in Financial Analysis, Fox School of Business, Temple University, USA Biswanath Bhattacharjee , Department of Management Science and Quantitative Methods, Gannon University, USA Sharmin Akter , Department of Information Technology Project Management, St. Francis College, USA Md Shakhaowat Hossain , Department of Management Science and Quantitative Methods, Gannon University, USA Afrin Hoque Jui , Department of Management Science and Quantitative Methods, Gannon University, USAAbstract
This study explores the application of sentiment analysis in the banking sector, focusing on customer feedback to enhance service quality and customer experiences. We collected a comprehensive dataset of approximately 100,000 entries from diverse sources, including customer satisfaction surveys, social media platforms, and direct feedback. A robust preprocessing pipeline was employed to address challenges associated with unstructured data, informal language, and mixed sentiments. We evaluated several machine learning and natural language processing models, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, Long Short-Term Memory (LSTM), and BERT (Bidirectional Encoder Representations from Transformers), using metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and training time. The results revealed that advanced models, particularly BERT, achieved superior performance with an accuracy of 88% and an F1 score of 0.86, demonstrating an exceptional ability to capture nuanced sentiments. This study underscores the importance of employing sophisticated sentiment analysis techniques in banking to derive actionable insights from customer feedback. The findings suggest that leveraging advanced models can significantly improve service quality and customer satisfaction, while also presenting avenues for future research into real-time sentiment analysis and its integration with customer relationship management systems.
ZENODO DOI:- https://doi.org/10.5281/zenodo.13981553
Keywords
Sentiment Analysis, Customer Feedback, Banking Services
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