  Unlike other languages, the Arabic language has a morphological complexity
which makes the Arabic sentiment analysis is a challenging task. Moreover, the
presence of the dialects in the Arabic texts have made the sentiment analysis
task is more challenging, due to the absence of specific rules that govern the
writing or speaking system. Generally, one of the problems of sentiment
analysis is the high dimensionality of the feature vector. To resolve this
problem, many feature selection methods have been proposed. In contrast to the
dialectal Arabic language, these selection methods have been investigated
widely for the English language. This work investigated the effect of feature
selection methods and their combinations on dialectal Arabic sentiment
classification. The feature selection methods are Information Gain (IG),
Correlation, Support Vector Machine (SVM), Gini Index (GI), and Chi-Square. A
number of experiments were carried out on dialectical Jordanian reviews with
using an SVM classifier. Furthermore, the effect of different term weighting
schemes, stemmers, stop words removal, and feature models on the performance
were investigated. The experimental results showed that the best performance of
the SVM classifier was obtained after the SVM and correlation feature selection
methods had been combined with the uni-gram model.
