Cross-lingual sentiment analysis has developed as a significant area of research in linguistics, especially for languages having diverse syntactic and morphological structures. The objective of this study emphasizes creating a sophisticated sentiment analysis model that connects English and Arabic datasets, two languages with distinct linguistic problems. Using cutting-edge transformer architectures, we utilize pre-trained models—BERT for English and AraBERT for Arabic—to address the challenges of morphologically rich but resource-limited languages such as Arabic. The foundation of this study is the IMDB movie review dataset, which is similarly structured and large for both languages. To find the best deep learning architecture, we conducted extensive experiments using Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and attention methods. While LSTM-based models produced competitive results, transformer-based models such as proposed BERT-LA that included bidirectional and attention layers outperformed them substantially, particularly on Arabic and English data. Furthermore, ablation research was conducted to evaluate the models' performance using important measures such as accuracy, precision, recall, and the F1-score. Our model got an impressive 97.04% accuracy on the English dataset and 98.02% on the Arabic dataset. This study contributes to understanding how language-specific embeddings and transformer models affect under-represented languages.
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Jefry et al. (2024) studied this question.