Journal of Digital Information Management Sadik Bessou, Rania Aberkane 17 5 2019 https://doi.org/10.6025/jdim/2019/17/5/289-295 http://dline.info/fpaper/jdim/v17i5/jdimv17i5_3.pdf This paper presents an approach based on supervised machine learning methods to discriminate between positive, negative and neutral Arabic reviews in online newswire. The corpus is labeled for subjectivity and sentiment analysis (SSA) at the sentence-level. The model uses both count and TF-IDF representations and apply six machine learning algorithms; Multinomial Naïve Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and k-nearest neighbors using uni-grams, bi-grams features. With the goal of extracting users' sentiment from written text. Experimental results showed that n-gram features could substantially improve performance; and showed that the Multinomial Naive Bayes approach is the most accurate in predicting topic polarity. Best results were achieved using count vectors trained by combination of word-based uni-gramsand bi-grams with an overall accuracy of 85.57% over two classes and 65.64% over three classes.