Causal Prompting for Implicit Sentiment Analysis with Large Language Models

2025-06-30

AI Summary

Implicit Sentiment Analysis (ISA) requires inferring unstated sentiment from contextual cues, yet existing large language model (LLM)-based Chain-of-Thought (CoT) methods rely on majority voting, ignoring the causal validity of reasoning paths and thus remaining vulnerable to spurious correlations and internal biases. To address this, we propose CAPITAL, the first CoT framework incorporating Front-Door Adjustment to explicitly model the causal pathway “input → reasoning chain → output.” CAPITAL integrates encoder-based clustering, Normalized Weighted Gaussian Mixture (NWGM) approximation, and contrastive learning to achieve causal-aware prompt optimization. Evaluated on multiple ISA benchmarks, CAPITAL significantly outperforms strong baselines, demonstrating superior robustness against adversarial perturbations and enhanced out-of-distribution generalization.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated, requiring models to perform deeper reasoning over subtle contextual cues. While recent prompting-based methods using Large Language Models (LLMs) have shown promise in ISA, they often rely on majority voting over chain-of-thought (CoT) reasoning paths without evaluating their causal validity, making them susceptible to internal biases and spurious correlations. To address this challenge, we propose CAPITAL, a causal prompting framework that incorporates front-door adjustment into CoT reasoning. CAPITAL decomposes the overall causal effect into two components: the influence of the input prompt on the reasoning chains, and the impact of those chains on the final output. These components are estimated using encoder-based clustering and the NWGM approximation, with a contrastive learning objective used to better align the encoder's representation with the LLM's reasoning space. Experiments on benchmark ISA datasets with three LLMs demonstrate that CAPITAL consistently outperforms strong prompting baselines in both accuracy and robustness, particularly under adversarial conditions. This work offers a principled approach to integrating causal inference into LLM prompting and highlights its benefits for bias-aware sentiment reasoning. The source code and case study are available at: https://github.com/whZ62/CAPITAL.

Problem

• Infer implied sentiment from subtle contextual cues
• Address biases in chain-of-thought reasoning for sentiment analysis
• Improve accuracy and robustness in implicit sentiment analysis

Innovation

• Causal prompting with front-door adjustment
• Encoder-based clustering for causal estimation
• Contrastive learning aligns encoder and LLM