SSAAR: An enhanced System for Sentiment Analysis of Arabic Reviews

Abstract

Sentiment Analysis, or Opinion Mining, has recently captivated the interest of scientists worldwide. With the increasing use of the internet, the web is becoming overloaded by data that contains useful information, which can be used in different fields. In fact, many studies have shed light on Sentiment Analysis of online data in different languages. However, the amount of research dealing with the Arabic language is still limited. In this paper, an empirical study is led to Sentiment Analysis of online reviews written in Modern Standard Arabic. A new system called SSAAR (System for Sentiment Analysis of Arabic Reviews) is proposed, allowing computational classification of reviews into three classes (positive, negative, neutral). The input data of this system is built by using a proposed framework called SPPARF (Scraping and double Preprocessing Arabic Reviews Framework), which generates a structured and clean dataset. Moreover, the provided system experiments two improved approaches for sentiment classification based on supervised learning, which are: Double preprocessing method and Feature selection method. Both approaches are trained by using five algorithms (Naïve Bayes, stochastic gradient descent Classifier (SGD), Logistic Regression, K-Nearest Neighbors, and Random Forest) and compared later under the same conditions. The experimental results show that the feature selection method using the SGD Classifier performs the best accuracy (77.1%). Therefore, the SSAAR System proved to be efficient and gives better results when using the feature selection method; nevertheless, satisfying results were obtained with the other approach, considered consequently suitable for the proposed system.