Enhancing Arabic Dialectal Sentiment Analysis through Advanced Data Augmentation Techniques
Year: 2025
Type: conference-paper
Abstract: This work addresses the challenge of Arabic sentiment analysis in the hospitality domain in all dialects by using data augmentation techniques.We created a pipeline with three simple techniques: context-based paraphrasing, pattern-based sentence generation, and domainspecific word replacement.Our method preserves the original dialect features, meanings, and key classification details while adding diversity to the training data.It also includes automatic fallback between methods to handle challenges effectively.We used the Fanar API for dialectal data augmentation in the hospitality domain.The AraBERT-Large-v02 model was fine-tuned on original and augmented data, showing improved performance.This study helps solve the problem of limited dialect data in Arabic NLP and offers an effective framework that is useful for other Arabic text analysis tasks.
Authors: Md. Rafiul Biswas, Wajdi Zaghouani
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7140086310