Advancements in Emotion Detection for Arabic Texts: A Comprehensive Review of Machine Learning and Deep Learning Approaches in Addressing Linguistic and Dialectal Complexities

Abstract

Emotion detection from text has become a vital area of research, offering valuable insights into human sentiment and reactions to global events. While significant progress has been made in this field, the Arabic language presents unique challenges due to its complex morphology, rich dialects, and the scarcity of high-quality public datasets. This paper provides a comprehensive survey of current approaches for Arabic text emotion detection, focusing on methods applied to publicly available datasets. This paper explores various machine learning and deep learning solutions that researchers have employed to overcome the linguistic complexities and data limitations. By analyzing these methodologies, we highlight key trends, identify the most effective techniques, and discuss the primary challenges that remain. Our work serves as a valuable resource for researchers and practitioners, summarizing the state-of-the-art in Arabic emotion detection and paving the way for future advancements in this challenging domain.