Abstract: Aspect-based sentiment analysis aims to identify the sentiment polarity of each aspect in a sentence.However,most existing approaches overlook the redundant and irrelevant information often present in review texts,which not only complicates model processing,but also hinders accurate sentiment element extraction.To address this issue,this paper proposes a model that transforms the original text into simplified clauses,expressing the same sentiment in a more concise manner.The key idea is to leverage a large language model to pre-identify aspect and opinion terms in the text,and then generate simplified clauses based on these identified sentiment elements.A self-verification mechanism is employed to ensure the generated clause satisfy three criteria:sentiment consistency,relevance,and conciseness.Furthermore,the model jointly uses both the original text and the simplified clauses to generate sentiment elements.Experimental results on public datasets—Restaurant,Laptop,and Phone,demonstrate that the model outperforms existing baselines,highlighting the significance of simplified clauses in aspect-based sentiment analysis.

Key words: Aspect-based sentiment analysis,Text simplification, Large language model,Self-validation,Natural language proces-sing