Enhancing Business Intelligence with Hybrid Transformers and Automated Annotation for Arabic Sentiment Analysis

Business is a key focus for many individuals, companies, countries and organisations. One effective way to enhance business performance is by analysing customer opinions through sentiment analysis. This technique offers valuable insights, known as business intelligence, which directly benefits business owners by informing their decisions and strategies. Substantial attention has been given to business intelligence through proposed machine learning approaches, deep learning models and approaches utilizing natural language processing methods. However, building a robust model to detect and identify users’ opinion and automated text annotation, particularly for the Arabic language, still faces many challenges. Thus, this study aims to propose a hybrid transfer learning model that uses transformers to identify positive and negative user comments that are related to business. This model consists of three pretrained models, namely, AraBERT, ArabicBERT, and XLM-RoBERTa. In addition, this study proposes a hybrid automatic Arabic annotation method based on CAMelBERT, TextBlob and Farasa to automatically classify user comments. A novel dataset, which is collected from user-generated comments (i.e. reviews on mobile apps), is introduced. This dataset is annotated twice using the proposed method and human-based annotation. Then, several experiments are conducted to evaluate the performance of the proposed model and the proposed annotation method. Experiment results show that the proposed hybrid model outperforms the baseline models, and the proposed annotation method achieves high accuracy, which is close to human-based annotation.

@article{Yafooz2024,
  title     = {Enhancing Business Intelligence with Hybrid Transformers and Automated Annotation for Arabic Sentiment Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wael M.S. Yafooz},
  doi       = {10.14569/IJACSA.2024.0150821},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150821}
}