Arabic Topic Identification: A Decade Scoping Review

Abstract

Arabic topic identification is a part of text classification that aims to assign a given text a set of pre-defined classes (i.e., topics) based on its content and extracted features. This task can be performed using rule-based methods or data-driven approaches. These latter gained more popularity since they require much less human effort to accurately classify a large number of documents. Due to the tremendous growth of Web contents primarily in news websites and social media, topic identification had received a great deal of attention over the last years, and has become a cornerstone of both search engines and information retrieval. The Arabic language is the fourth most used language on the web and records the highest growth in the last two decades (2000–2020). Based on these facts currently available, it seems fair to look closer at the advancements in the Arabic topic identification in the last decade. To this end, we performed the first of its kind scoping review that addresses recent studies in the field of Arabic topic identification that follows the PRISMA-ScR guidelines. This review is based on various online bibliographic databases (e.g., Springer, ScienceDirect, and IEEE Xplore) and datasets search engines (e.g., Google Dataset Search).