Application of Multiple Chain-of-Thought in Contrastive Reasoning for Implicit Sentiment Analysis

• URL: http://arxiv.org/abs/2503.07140v1
• Date: Mon, 10 Mar 2025 10:10:50 GMT
• Title: Application of Multiple Chain-of-Thought in Contrastive Reasoning for Implicit Sentiment Analysis
• Authors: Liwei Yang, Xinying Wang, Xiaotang Zhou, Zhengchao Wu, Ningning Tan,
• Abstract summary: Implicit sentiment analysis aims to uncover emotions that are subtly expressed, often obscured by ambiguity and figurative language.<n>We propose a novel Dual Reverse Chain Reasoning framework to enhance the performance of implicit sentiment analysis.<n>We also introduce a Triple Reverse Chain Reasoning framework to address the limitations of random hypotheses.
• Score: 1.9472869221587836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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