Enhancing Customer Experience Through Arabic Aspect-Based Sentiment Analysis of Saudi Reviews

Big brands thrive in today's competitive marketplace by focusing on customer experience through product reviews. Manual analysis of these reviews is laborintensive, necessitating automated solutions. This paper conducts aspect-based sentiment analysis on Saudi dialect product reviews using machine learning and NLP techniques. Addressing the lack of datasets, we create a unique dataset for Aspect-Based Sentiment Analysis (ABSA) in Arabic, focusing on the Saudi dialect, comprising two manually annotated datasets of 2000 reviews each. We experiment with feature extraction techniques such as Part-of-Speech tagging (POS), Term Frequency-Inverse Document Frequency (TF-IDF), and n-grams, applying them to machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN). Our results show that for electronics reviews, RF with TF-IDF, POS tagging, and tri-grams achieves 86.26% accuracy, while for clothes reviews, SVM with TF-IDF, POS tagging, and bi-grams achieves 86.51% accuracy.