Sentiment analysis is a field of research that consists in analyzing the sensations, attitudes, and emotions of individuals towards entities such as products, services, and economic organizations. Likewise, the demand for Arabic Sentiment Analysis has grown rapidly due to the extensive use of the Arabic language in social media networks and has generated considerable interest from the research community. Arabic is one of the widely used languages on social networks. However, its morphological complexities, its dialect varieties, and its relatively few resources make it a challenging language for sentiment analysis. The main goal of our study is to implement and compare the performance of ASA by exploiting machine learning and deep learning models to automatically determine the sentiments by classifying them as positive or negative. For that, this study implements and evaluates a deep learning model namely the long short-term memory (LSTM) model, and three machine learning algorithms: Support vector machines (SVM), Logistic Regression (LR), K-Nearest neighbours (KNN). These classifiers are applied on the Arabic-Review (ARev) database that is manually annotated and collected from many Arabic resources. The results show that SVM and LR models are the best performing classifiers with an accuracy of 92% and 93% respectively.