A Survey on Aspect-Based Sentiment Quadruple Extraction with Implicit Aspects and Opinions

Abstract Quadruple sentiment analysis is an essential task in aspect-based sentiment analysis that aims to understand people's viewpoints by analyzing four elements in text: category, aspect, opinion, and sentiment polarity. In recent years, quadruple sentiment analysis tasks have received extensive research attention for capturing comprehensive sentiment information in sentences. This paper focuses on the latest progress in quadruple sentiment analysis tasks. Additionally, the paper provides a summary of the application of methods based on pre-trained language models and datasets in quadruple sentiment analysis with implicit aspects and opinions. It discusses techniques for constructing more practical quadruple sentiment analysis systems in generative large model scenarios. The paper's final section outlines quadruple sentiment analysis research's current challenges and future directions. Moreover, the paper suggests that using advanced language models, such as ChatGPT, holds promise for improving the accuracy and efficiency of quadruple sentiment analysis. Overall, this paper sheds light on the importance of quadruple sentiment analysis and highlights opportunities for further research in this field.