  In this study, we aimed to improve the performance results of Arabic
sentiment analysis. This can be achieved by investigating the most successful
machine learning method and the most useful feature vector to classify
sentiments in both term and document levels into two (positive or negative)
categories. Moreover, specification of one polarity degree for the term that
has more than one is investigated. Also to handle the negations and
intensifications, some rules are developed. According to the obtained results,
Artificial Neural Network classifier is nominated as the best classifier in
both term and document level sentiment analysis (SA) for Arabic Language.
Furthermore, the average F-score achieved in the term level SA for both
positive and negative testing classes is 0.92. In the document level SA, the
average F-score for positive testing classes is 0.94, while for negative
classes is 0.93.
