Arabic exhibits a rich and intricate linguistic landscape, with Modern Standard Arabic (MSA) serving as the formal written and spoken medium, alongside a wide variety of regional dialects used in everyday communication. These dialects vary considerably in syntax, vocabulary, phonology, and meaning, presenting significant challenges for natural language processing (NLP). The complexity is particularly pronounced in sentiment analysis, where emotional expressions and idiomatic phrases differ markedly across regions, hindering consistent and accurate sentiment detection. This paper describes our submission to the Ahasis Shared Task: A Benchmark for Arabic Sentiment Analysis in the hospitality domain. This shared task focuses on advancing sentiment analysis techniques for Arabic dialects in the hotel domain. Our proposed approach achieved an F1 score of 0.88 % on the internal test set (split from the original training data), and 79.16% on the official hidden test set of the shared task. This performance secured our team second place in the Ahasis Shared Task.

It is essential to understand the attitude of individuals towards specific topics in Arabic language for tasks like sentiment analysis, opinion mining, and social media monitoring. However, the diversity of the linguistic characteristics of the Arabic language presents several challenges to accurately evaluate the stance. In this study, we suggest ensemble approach to tackle these challenges. Our method combines different classifiers using the voting method. Through multiple experiments, we prove the effectiveness of our method achieving significant F1-score value equal to 0.7027. Our findings contribute to promoting NLP and offer treasured enlightenment for applications like sentiment analysis, opinion mining, and social media monitoring.