Sarcasm detection remains a challenging task in Natural Language Processing (NLP), especially for low‑resource and non‑standardized languages. Hence, this study addresses Franco‑Arabic, a widely used form of online communication where Arabic words are written with Latin characters and numerals. Its informal nature and orthographic variation complicate sarcasm identification and limit the applicability of existing NLP models. We propose an approach that integrates transformer‑based representations with auxiliary linguistic features and rule‑based cues to capture both contextual meaning and sentimentdriven inconsistencies. This research opens the door to practical applications. In particular, future work will investigate integrating sarcasm detection into the marketing sector, where accurate recognition of sarcastic reviews can enhance sentiment analysis, customer segmentation, and personalized communication strategies.
Hassan et al. (2026) studied this question.