In-context learning enhanced by multi-perspective sequential retrieval and predictive feedback for few-shot aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to extract fine-grained opinions from the text by discerning sentiments toward specific aspects. Although large language models (LLMs) perform well in in-context learning (ICL), current ICL methodologies typically retrieve semantically similar but structurally redundant examples, failing to capture syntactic and aspect-level cues critical for ABSA. To overcome these limitations, we report Multi-perspective Sequential retrieval with Predictive Feedback (MSPF), a few-shot learning framework that enhances ICL through MSPF, which integrates three complementary perspectives: overall semantic, syntactic relevance, and aspect sentiment alignment. Evaluated on four benchmark datasets (Laptop14, Restaurant14, Books, and Clothing), MSPF achieved F1 scores of 67.03% (Laptop14), 73.51% (Restaurant14), 76.07% (Books), and 81.96% (Clothing), outperforming standard ICL by +7.06%, +5.60%, +25.61%, and +18.38%, respectively. These results validated the efficacy of MSPF in improving LLM reasoning for fine-grained sentiment tasks with limited annotations.