Articles
| Open Access |
DOI: SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS
Rowsan Jahan Bhuiyan , Master of Science in Information Technology, Washington University of Science and Technology, USASalma Akter , Department of Public Administration, Gannon University, Erie, PA, USA
Aftab Uddin , Fox School of Business & Management, Temple University, USA
Md Shujan Shak , Master of Science in Information Technology, Washington University of Science and Technology, USA
Md Rasibul Islam , Department of Management Science and Quantitative Methods, Gannon University, USA
Md Redowan Amin Mollick , Master of Science in Data Analytics and Strategic Business Intelligence, Long Island University post, USA
S M Shadul Islam Rishad , Master of Science in Information Technology, Westcliff University, USA
Farzana Sultana , Department of Marketing & Business Analytics, Texas A&M University-Commerce, USA
Md. Hasan-Or-Rashid , Department of Marketing & Business Analytics, Texas A&M University-Commerce, USA
Abstract
This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.
zenodo DOI:- https://doi.org/10.5281/zenodo.13908078
Keywords
stemming, positive, neutral, learning models
References
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Copyright (c) 2024 Miah , Jonayet, Rowsan Jahan Bhuiyan, Salma Akter, Aftab Uddin, Md Shujan Shak, Md Rasibul Islam, S M Shadul Islam Rishad, Farzana Sultana, Md. Hasan-Or-Rashid
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