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Unlike other languages, the Arabic language has a morphological complexitywhich makes the Arabic sentiment analysis is a challenging task. Moreover, thepresence of the dialects in the Arabic texts have made the sentiment analysistask is more challenging, due to the absence of specific rules that govern thewriting or speaking system. Generally, one of the problems of sentimentanalysis is the high dimensionality of the feature vector. To resolve thisproblem, many feature selection methods have been proposed. In contrast to thedialectal Arabic language, these selection methods have been investigatedwidely for the English language. This work investigated the effect of featureselection methods and their combinations on dialectal Arabic sentimentclassification. The feature selection methods are Information Gain (IG),Correlation, Support Vector Machine (SVM), Gini Index (GI), and Chi-Square. Anumber of experiments were carried out on dialectical Jordanian reviews withusing an SVM classifier. Furthermore, the effect of different term weightingschemes, stemmers, stop words removal, and feature models on the performancewere investigated. The experimental results showed that the best performance ofthe SVM classifier was obtained after the SVM and correlation feature selectionmethods had been combined with the uni-gram model.

A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine