Methodologies and their comparison in complex compound aspect-based sentiment analysis: A survey

Abstract

Sentiment analysis as a part of natural language processing (NLP) has received much attention following the demand to understand people’s opinions. Aspect-based sentiment analysis (ABSA) is a fine-grained task from sentiment analysis that aims to classify the sentiment at the aspect level. Throughout the years, researchers have formulated ABSA into various tasks for different scenarios. Unlike early works, current ABSA tasks utilize many elements to provide more details to produce informative results. However, it is difficult to completely explore the works of ABSA because of the many different tasks, terms, and results. This paper surveyed recent studies on ABSA, specifically on its complex compound tasks. We investigated some key elements, problem formulations, and datasets currently utilized by most ABSA communities. We focused on reviewing the latest methodologies and worked to find the current state-of-the-art methodologies by performing a comparative analysis. From our study, we found that there has been a shift to generative methods in solving the ABSA problem, which signifies the evolving emphasis on holistic, end-to-end approaches. Finally, we identified some open challenges and future directions for ABSA research.