Sentiment Analysis in Arabic is a challenging task due to the rich morphology of the language. Moreover, the task is further complicated when applied to Twitter data that is known to be highly informal and noisy. In this paper, we develop a hybrid method for sentiment analysis for Arabic tweets for a specific Arabic dialect which is the Saudi Dialect. Several features were engineered and evaluated using a feature backward selection method. Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way). The best F1-score for each of these models was (69.9,61.63,55.07) respectively.
翻译：阿拉伯语的感官分析是一项具有挑战性的任务,因为阿拉伯语具有丰富的形态,此外,在应用已知非常非正式和吵闹的Twitter数据时,任务就更加复杂。在本文中,我们开发了一种混合方法,用于对特定阿拉伯方言即沙特方言的阿拉伯推文进行情绪分析,利用特征落后选择方法设计和评价了几个特征。然后,为若干分类模式(双向、三向、四道)开发了一种混合方法,将基于人身和基于词汇的方法结合起来。每种模式的最佳F1核心分别是(69.9,61.63,55.07)。
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