Sentiment Analysis of Digital Banking Reviews Using Machine Learning and Large Language Models
Year: 2025
Type: article
Abstract: Sentiment analysis, in the context of digital banking reviews, aims to assess customer satisfaction and support service enhancement. Despite increasing attention to sentiment analysis across domains, Arabic banking reviews remain underexplored. To bridge this gap, we introduce a dataset of 4922 Arabic reviews from three major Saudi digital banks with three sentiment categories positive, negative, or conflict—providing actionable insights for banks. We evaluate the dataset using several machine learning models and four large language models (LLMs)—GPT 3.5, GPT 4, Llama-3-8B-Instruct, and SILMA—using zero-shot (no labeled examples) and few-shot (a few labeled examples) learning strategies. Our results show that GPT 4 performs best among LLMs in few-shot settings, while traditional models still outperform LLMs, with a Voting Classifier achieving 90.24% accuracy. This study contributes a domain-specific dataset and comparative analysis to support research and practical improvements in Arabic digital banking services.
Source: Electronics
Authors: Raghad Alawaji, Abdulrahman Aloraini
Institution: Qassim University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: Qassim University
OpenAlex ID: W4410628960