In this paper, we describe our contribution in Ahasis shared task: Sentiment analysis on Arabic Dialects in the Hospitality Domain. Through the presented framework, we explored using two learning strategies tailored to a Large Language Model (LLM) and Transformer-based model variants. While few-shot prompting was used with GPT-4o, fine-tuning was adopted once to refine the essential MARBERT model on the Ahasis dataset and then to utilize a MARBERT variant model, SODA-BERT, that was pretrained on an Omani sentiment dataset and later evaluated with the shared task data.