  The complexities of Arabic language in morphology, orthography and dialects
makes sentiment analysis for Arabic more challenging. Also, text feature
extraction from short messages like tweets, in order to gauge the sentiment,
makes this task even more difficult. In recent years, deep neural networks were
often employed and showed very good results in sentiment classification and
natural language processing applications. Word embedding, or word distributing
approach, is a current and powerful tool to capture together the closest words
from a contextual text. In this paper, we describe how we construct Word2Vec
models from a large Arabic corpus obtained from ten newspapers in different
Arab countries. By applying different machine learning algorithms and
convolutional neural networks with different text feature selections, we report
improved accuracy of sentiment classification (91%-95%) on our publicly
available Arabic language health sentiment dataset [1]
