Surveying the Landscape: Compound Methods for Aspect-Based Sentiment Analysis

Aspect-based sentiment Analysis (ABSA) has emerged as a critical research area in natural language processing, facilitating a deeper understanding of user opinions and sentiments expressed in text. This review article comprehensively surveys the landscape of deep learning approaches in ABSA, focusing on triplet and quadruplet ABSA. We delve into the significance of ABSA in diverse domains and present an overview of the critical components and challenges associated with compound ABSA tasks. The review analyzes state-of-the-art models, encompassing pipeline-based and generative-based solutions. Comparative analysis demonstrates the advantages and limitations of these approaches, including their performance, generalizability, and efficiency. Additionally, we explore the domains and datasets used in ABSA research and highlight the crucial factors contributing to ABSA task solutions' effectiveness. This comprehensive review highlights current challenges and fosters further advancements in compound ABSA tasks.