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.

Related papers

• How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision? [45.11635323173876]
  We conduct a comprehensive analysis of latent reasoning methods to better understand the role and behavior of latent representation in the process.<n>We find that while latent representations can encode multiple possibilities, the reasoning process does not faithfully implement structured search.<n>Our findings reveal a trade-off associated with supervision strength: stronger supervision mitigates shortcut behavior but restricts the ability of latent representations to maintain diverse hypotheses.
  arXiv  Detail & Related papers  (2026-02-25T22:00:59Z)
• Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
  We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
  arXiv  Detail & Related papers  (2026-01-07T16:39:34Z)
• Learning What to Attend First: Modality-Importance-Guided Reasoning for Reliable Multimodal Emotion Understanding [50.014363382140324]
  Modality Importance (MI) is a simple yet effective mechanism for identifying the emotion-dominant modality.<n>MIGR reorganizes reasoning sequences so that explanations begin from the modality most critical to the target emotion.<n>Results show that MIGR substantially improves reasoning reliability, decreasing instances of correct predictions accompanied by emotionally inconsistent explanations.
  arXiv  Detail & Related papers  (2025-12-02T12:29:41Z)
• Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model [64.31242163019242]
  Deductive and abductive reasoning are critical paradigms for analyzing knowledge graphs.<n>We propose a unified framework for Deductive and Abductive Reasoning in Knowledge graphs, called DARK.<n>We show that DARK achieves state-of-the-art performance on both deductive and abductive reasoning tasks.
  arXiv  Detail & Related papers  (2025-10-13T14:34:57Z)
• CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
  We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
  arXiv  Detail & Related papers  (2025-06-05T17:02:52Z)
• Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment [54.62926010621013]
  We introduce a novel task, code reasoning, to provide a new perspective for the reasoning abilities of large language models.<n>We summarize three meta-benchmarks based on established forms of logical reasoning, and instantiate these into eight specific benchmark tasks.<n>We present a new pathway exploration pipeline inspired by human intricate problem-solving methods.
  arXiv  Detail & Related papers  (2025-02-17T10:39:58Z)
• PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis [74.41260927676747]
  This paper bridges the gaps by introducing a multimodal conversational Sentiment Analysis (ABSA) To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit and explicit sentiment elements. To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism.
  arXiv  Detail & Related papers  (2024-08-18T13:51:01Z)
• Rethinking harmless refusals when fine-tuning foundation models [0.8571111167616167]
  We investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. We identify a pervasive phenomenon we term emphreason-based deception, where models either stop producing reasoning traces or produce seemingly ethical reasoning traces that belie the unethical nature of their final outputs.
  arXiv  Detail & Related papers  (2024-06-27T22:08:22Z)
• Contrastive Chain-of-Thought Prompting [74.10511560147293]
  We propose contrastive chain of thought to enhance language model reasoning. Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations. Our experiments on reasoning benchmarks demonstrate that contrastive chain of thought can serve as a general enhancement of chain-of-thought prompting.
  arXiv  Detail & Related papers  (2023-11-15T18:54:01Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• Towards Trustworthy Explanation: On Causal Rationalization [9.48539398357156]
  We propose a new model of rationalization based on two causal desiderata, non-spuriousness and efficiency. The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets.
  arXiv  Detail & Related papers  (2023-06-25T03:34:06Z)
• Causal Intervention Improves Implicit Sentiment Analysis [67.43379729099121]
  We propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV) We first review sentiment analysis from a causal perspective and analyze the confounders existing in this task. Then, we introduce an instrumental variable to eliminate the confounding causal effects, thus extracting the pure causal effect between sentence and sentiment.
  arXiv  Detail & Related papers  (2022-08-19T13:17:57Z)
• Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis [56.84237932819403]
  This paper aims to estimate and mitigate the bad effect of textual modality for strong OOD generalization. Inspired by this, we devise a model-agnostic counterfactual framework for multimodal sentiment analysis.
  arXiv  Detail & Related papers  (2022-07-24T03:57:40Z)
• An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation [21.106357884651363]
  We introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains. We propose a two-phase approach that consists of a hypothesis generator and a reasoner. The whole system is trained by exploiting raw textual dialogues without using any reasoning chain annotations.
  arXiv  Detail & Related papers  (2022-03-11T10:44:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.