  One of the main difficulties in sentiment analysis of the Arabic language is
the presence of the colloquialism. In this paper, we examine the effect of
using objective words in conjunction with sentimental words on sentiment
classification for the colloquial Arabic reviews, specifically Jordanian
colloquial reviews. The reviews often include both sentimental and objective
words, however, the most existing sentiment analysis models ignore the
objective words as they are considered useless. In this work, we created two
lexicons: the first includes the colloquial sentimental words and compound
phrases, while the other contains the objective words associated with values of
sentiment tendency based on a particular estimation method. We used these
lexicons to extract sentiment features that would be training input to the
Support Vector Machines (SVM) to classify the sentiment polarity of the
reviews. The reviews dataset have been collected manually from JEERAN website.
The results of the experiments show that the proposed approach improves the
polarity classification in comparison to two baseline models, with accuracy
95.6%.
