Adapted English sentiment tools improve Arabic dialect classification accuracy
BARRAC: Adaptation of an English Aspect-based Sentiment Analysis Approach for Classification Tasks in Arabic Dialects
Computation and LanguageArtificial Intelligence
Summary
Sentiment analysis helps computers understand how people feel in text. This paper looks at whether methods made for English can work well for Arabic dialects. The authors created BARRAC by changing parts of an English tool to better fit Arabic language markers like sarcasm and dialect differences. BARRAC outperformed previous Arabic methods and even did better than a powerful AI model called GPT-4o on most tested tasks. This shows it's helpful to adapt language-specific tools, not just models and data, when working with Arabic.
What this means in practice
- •For arabic nlp developers: Improve sentiment classification systems for different Arabic dialects by integrating linguistically informed markers tailored to Arabic.
- •For customer feedback analysts: Analyze Arabic dialect customer reviews with better accuracy in sentiment and sarcasm understanding for improved product insights.
Authors
Ali Almutairi, Gelareh Mohammadi, Imran Razzak, Aditya Joshi
Abstract
With the rapid growth of Arabic NLP, several models, datasets and benchmarks have been reported. This paper asks whether approaches developed for majority languages like English can be adapted to Arabic tasks. We adapt an English aspect-based sentiment analysis framework to Arabic classification tasks and present the adaptation as BARRAC: Brainstorming Alignment and Replaced Representation learning for ArabiC tasks. BARRAC replaces consumer-review attribute pools with Arabic linguistic devices and markers for dialectal sentiment, sarcasm, and dialect identification, and replaces noisy self-training with two-stage training. Evaluated on five Arabic dialect datasets, BARRAC achieves a mean macro-F1 of 63.93\%, outperforming the best few-label SOTA by 3\%, and outperforming GPT-4o on four out of five tasks. Error analysis provides insights into remaining challenges. These results demonstrate that adapting task-specific approaches is a promising direction for Arabic NLP alongside adapting models, datasets and benchmarks.