Steering a Standard Arab Language Processing Model Towards Accurate Saudi Dialect Sentiment Analysis Using Generative AI

Sentiment analysis (SA) is crucial for many NLP applications across various domains. While Arabic is one of the world's major languages, high-quality NLP models developed for standard Arabic often underperform on regional dialects like the Saudi Dialect (SD) due to a lack of SD-specific training data. This paper presents a novel approach to adapting a high-resource language model, AraBERT, for low-resource dialect sentiment analysis by combining minimal SD data collection with generative AI. In the absence of openly accessible SD datasets, we augmented a small amount of collected SD data with GPT-generated SD data to fine-tune AraBERT for sentiment analysis in SD. Our contributions include (1) demonstrating the feasibility of low-effort data collection of a low-resource dialect for adapting existing high-resource NLP models and (2) leveraging GPT-generated data to augment collected data to enhance a high-resource language model for sentiment classification in a low-resource dialect, achieving significant improvements over the pre-trained high-resource model. These two contributions imply a potentially replicable approach that can serve as a template for future research in other low-resource NLP tasks. This paper presents a promising solution for enhancing model performance in low-resource dialects and has implications for similar under-resourced languages.