Lexicon annotation in sentiment analysis for dialectal Arabic: Systematic review of current trends and future directions

Abstract

Due to the vast volumes of newly streamed data on the Internet and social media, the use of sentiment analysis (SA) to extract information and analyze people's opinions has become a trendy topic. Yet, the majority of research are attributed to the English language, despite the fact that other languages, such as Arabic, are among the most popular on the Internet. Considering the availability of numerous dialects of this language and how their data were annotated and processed, the absence of research in this field is evident. Understanding these initiatives merits a great deal of attention in Arabic SA research. To the best of our knowledge, this domain has not been considered before, and thus the aim of this study is to perform a systematic review with regard to SA and data annotations for Arabic dialects published between 2015 and 2023. The outcomes of this research offer a refined taxonomy of data annotation methods classified into three categories: (1) manual, (2) automatic, and (3) hybrid methods. In addition, a discussion of the research challenges, motivations, and recommendations is presented with detailed taxonomy analysis of current research trends, and from this, we identify new research gaps and propose new research implications and future directions that will encourage more scholars to contribute to Arabic SA research, facilitate more successful multilingual SA applications, and provide insights regarding Arabic SA in different contexts.