The hospitality industry in the Arab world increasingly relies on customer feedback to shape services, driving the need for advanced Arabic sentiment analysis tools. To address this challenge, the Sentiment Analysis on Arabic Dialects in the Hospitality Domain shared task focuses on Sentiment Detection in Arabic Dialects. This task leverages a multi-dialect, manually curated dataset derived from hotel reviews originally written in Modern Standard Arabic (MSA) and translated into Saudi and Moroccan (Darija) dialects. The dataset consists of 538 sentiment-balanced reviews spanning positive, neutral, and negative categories. Translations were validated by native speakers to ensure dialectal accuracy and sentiment preservation. This resource supports the development of dialect-aware NLP systems for real-world applications in customer experience analysis. More than 40 teams have registered for the shared task, with 12 submitting systems during the evaluation phase. The top-performing system achieved an F1 score of 0.81, demonstrating the feasibility and ongoing challenges of sentiment analysis across Arabic dialects.

翻译：阿拉伯世界的酒店业日益依赖客户反馈来塑造服务，这推动了对先进阿拉伯语情感分析工具的需求。为应对这一挑战，酒店领域阿拉伯语方言情感分析共享任务聚焦于阿拉伯语方言的情感检测。该任务利用一个多方言、人工标注的数据集，该数据集源自最初以现代标准阿拉伯语（MSA）撰写并翻译为沙特和摩洛哥（Darija）方言的酒店评论。数据集包含538条情感平衡的评论，涵盖积极、中性和消极类别。翻译由母语者验证，以确保方言准确性和情感保留。这一资源支持开发面向客户体验分析实际应用的方言感知NLP系统。已有超过40支团队注册参与该共享任务，其中12支在评估阶段提交了系统。表现最佳的系统取得了0.81的F1分数，证明了跨阿拉伯语方言情感分析的可行性及持续存在的挑战。