Sentiment Analysis Methods for Arabic Content on Social Media: A Systematic Review

Abstract

The topics of sentiment analysis either written or auditory texts are among important research areas in artificial intelligence (AI). The researchers in Natural Processing language (NLP) concerned more about the development of sentiment analysis methods and applications. For the available literature, the English Language has most research studies for the sentiment analysis. Arabic language is a membership in an entirely distinct language family than English explains why it needs the researchers to explore it from the scratch. Consequently, languages other than Arabic are more closely connected to one another than Arabic is. The Indo-European languages are utterly alien to the grammar, syntax, pronunciation, and lexicon. As a result, Arabic sentiment analysis has recently attracted the attention of the scientific researchers. This is due to the huge number of ideas and thoughts that are posted daily by social media users around the world. Manual processing of such huge data to obtain valuable information is an impossible task. The aim of this systematic review is to present a comprehensive review the major contributions in the field of Arabic sentiment analysis (ASA). The previous studies were primarily focused on dealing with certain sentiment analysis tasks, according to a comprehensive analysis of the accessible literature. The approaches found in the literature for ASA is classified into three main groups: (i) supervised, (ii) unsupervised, and (iii) hybrid. The literature's primary points include the fact that sentiment analysis in Arabic is difficult due to the language's complexity and wide variety of local dialects. These research findings, while intriguing, were not all in agreement. This difference is mostly attributable to the method chosen, the job being examined, besides the peculiarities and nuances of the Arabic diversity being studied. The evaluation of the literature revealed that, Naï ve bayes (NB), K-Nearest Neighbour (KNN) and Support Victor Machine (SVM) are among the most popular classifiers applied to ASA. The lack of trusted Arabic data sets to allow the researchers examine the proposed ASA methods is among the main issues not yet solved. Therefore, this research can help the researchers to get updated about the literature related to Arabic sentiment analysis datasets and existed methods and techniques.