A systematic literature review of Arabic dialect sentiment analysis
Yassir Matrane, Faouzia Benabbou, Nawal Sael
Abstract
Sentiment analysis is the process of using natural language processing, computational linguistics, and other text analysis techniques to identify and extract subjective information in order to generate a judgment about the attitude or emotional state behind the text. It has been applied to many fields, including marketing, politics, and psychology. This paper presents a systematic literature review (SLR) of sentiment analysis for dialectical Arabic (DA). The variation among these dialects is primarily based on differences in grammar, vocabulary, and syntax, which makes it hard for researchers to perform polarity classification for DA. This is where our SLR comes in, assessing multiple aspects of sentiment analysis for DA as well as smoothing the advancement of researchers' works for related studies. We have identified all the steps that have a crucial influence on the machine learning model applied for dialect sentiment analysis, including text annotation, text preprocessing, feature extraction, and the approaches adopted. We have also determined the challenges and open issues of sentiment analysis for Arabic dialect (SAAD), where research efforts should be focused.