Large language models (LLMs) have achieved promising results in sentiment
analysis through the in-context learning (ICL) paradigm. However, their ability
to distinguish subtle sentiments still remains a challenge. Inspired by the
human ability to adjust understanding via feedback, this paper enhances ICL by
incorporating prior predictions and feedback, aiming to rectify sentiment
misinterpretation of LLMs. Specifically, the proposed framework consists of
three steps: (1) acquiring prior predictions of LLMs, (2) devising predictive
feedback based on correctness, and (3) leveraging a feedback-driven prompt to
refine sentiment understanding. Experimental results across nine sentiment
analysis datasets demonstrate the superiority of our framework over
conventional ICL methods, with an average F1 improvement of 5.95%.

通过引入先前的预测和反馈来增强上下文学习模型（LLMs）在情感分析中的分辨能力，并通过使用反馈驱动的提示来改进情感理解。实验结果显示，这个框架在九个情感分析数据集上表现优于传统的上下文学习方法，平均 F1 值提高了 5.95%。