多重思维链在隐式情感分析对比推理中的应用
Application of Multiple Chain-of-Thought in Contrastive Reasoning for Implicit Sentiment Analysis
AI总结：
本文提出双重和三重反向链推理框架，通过假设、否定与对比的多步推理机制提升隐式情感分析性能，并在多模型规模上取得最优结果。
摘要：
隐式情感分析旨在揭示那些往往因歧义和比喻性语言而被掩盖的微妙情感。为了完成这一任务，需要借助大语言模型和多步推理来识别那些未被明确表达的情感。在本研究中，我们提出了一种新颖的双重反向链推理（DRCR）框架，以提升隐式情感分析的性能。受演绎推理启发，该框架包含三个关键步骤：1）假设一种情感极性并推导出推理过程，2）否定初始假设并推导出新的推理过程，3）对比两条推理路径以推断最终的情感极性。在此基础上，我们还引入了三重反向链推理（TRCR）框架，以解决随机假设的局限性。两种方法均结合了对比机制和多步推理，显著提高了隐式情感分类的准确性。实验结果表明，这两种方法在不同模型规模上均优于现有方法，达到了最先进的性能。这验证了将对比推理与多步推理相结合用于隐式情感分析的有效性。
英文摘要：
Implicit sentiment analysis aims to uncover emotions that are subtly expressed, often obscured by ambiguity and figurative language. To accomplish this task, large language models and multi-step reasoning are needed to identify those sentiments that are not explicitly stated. In this study, we propose a novel Dual Reverse Chain Reasoning (DRCR) framework to enhance the performance of implicit sentiment analysis. Inspired by deductive reasoning, the framework consists of three key steps: 1) hypothesize an emotional polarity and derive a reasoning process, 2) negate the initial hypothesis and derive a new reasoning process, and 3) contrast the two reasoning paths to deduce the final sentiment polarity. Building on this, we also introduce a Triple Reverse Chain Reasoning (TRCR) framework to address the limitations of random hypotheses. Both methods combine contrastive mechanisms and multi-step reasoning, significantly improving the accuracy of implicit sentiment classification. Experimental results demonstrate that both approaches outperform existing methods across various model scales, achieving state-of-the-art performance. This validates the effectiveness of combining contrastive reasoning and multi-step reasoning for implicit sentiment analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
