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Engineering and Technology OPEN ACCESS

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, USA
Salma 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
Sakib Salam Jamee
Department of Management Information Systems, University of Pittsburgh, PA, 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
DOI Crossref DOI 10.37547/tajet/Volume06Issue10-07
tajet 2024
VOL. 6 / NO. 10 OCTOBER
FULL TEXT PP. 54-66
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VOLUME 6
ISSUE 10
YEAR 2024
PAGES 54-66
RESEARCH OVERVIEW

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

RESEARCH TOPICS

Keywords

stemming positive neutral learning models

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References

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