  Arabic sentiment analysis has become an important research field in recent
years. Initially, work focused on Modern Standard Arabic (MSA), which is the
most widely-used form. Since then, work has been carried out on several
different dialects, including Egyptian, Levantine and Moroccan. Moreover, a
number of datasets have been created to support such work. However, up until
now, less work has been carried out on Sudanese Arabic, a dialect which has 32
million speakers. In this paper, two new publicly available datasets are
introduced, the 2-Class Sudanese Sentiment Dataset (SudSenti2) and the 3-Class
Sudanese Sentiment Dataset (SudSenti3). Furthermore, a CNN architecture, SCM,
is proposed, comprising five CNN layers together with a novel pooling layer,
MMA, to extract the best features. This SCM+MMA model is applied to SudSenti2
and SudSenti3 with accuracies of 92.75% and 84.39%. Next, the model is compared
to other deep learning classifiers and shown to be superior on these new
datasets. Finally, the proposed model is applied to the existing Saudi
Sentiment Dataset and to the MSA Hotel Arabic Review Dataset with accuracies
85.55% and 90.01%.
