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

arXiv: https://arxiv.org/abs/2503.07140 · HTML: https://mlsanity.com/paper/2503.07140/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 3 of its 3 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 3 of 3)

- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.11255/) (2023)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [Causal Intervention Improves Implicit Sentiment Analysis](https://mlsanity.com/paper/2208.09329/) (2022)

## Built on by (most cited first, 1 of 1)

- [AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes](https://mlsanity.com/paper/2608.26193/) (2026)

## Abstract (arXiv)

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.