Duality-Driven Aspect Sentiment Triplet Extraction with LLM and Iterative Reinforcement

Aspect-based sentiment triplet extraction tasks remain a long-standing challenge, which aim to achieve aspect, opinion, and sentiment polarity from sentences. Most existing methods achieve excellent performance by exploring the interactions between aspect and opinion terms. However, few studies focus on the positive impact of sentiment on triplet extraction. As sentiment acts as a key cue in triplet extraction, its role is often overlooked, thereby limiting extraction performance. This paper proposes a novel framework, duality-driven aspect sentiment triplet extraction with a large language model and iterative reinforcement, which integrates duality-driven with a large language model for the aspect sentiment triplet task. This study employs a duality-driven strategy based on symmetry to extract aspect-based sentiment triplets, fully taking into account sentiment polarity during the interaction between aspects and opinions. Moreover, this study devises a two-view prompt template for prior knowledge fusion based on large language models and employs confidence cycle iteration strategies to alleviate cascading errors. Extensive experiments show that the framework outperforms the previous state-of-the-art model. These findings demonstrate that the proposed model makes a positive impact on the aspect sentiment triplet extraction task overall.