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 labor-intensive, 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.

@article{Alrefae2024,
  title     = {Enhancing Customer Experience Through Arabic Aspect-Based Sentiment Analysis of Saudi Reviews},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Razan Alrefae and Revan Alqahmi and Munirah Alduraibi and Shatha Almatrafi and Asmaa Alayed},
  doi       = {10.14569/IJACSA.2024.0150742},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150742}
}