CDEA: Causality-Driven Dialogue Emotion Analysis via LLM

With the rapid advancement of human–machine dialogue technology, sentiment analysis has become increasingly crucial. However, deep learning-based methods struggle with interpretability and reliability due to the subjectivity of emotions and the challenge of capturing emotion–cause relationships. To address these issues, we propose a novel sentiment analysis framework that integrates structured commonsense knowledge to explicitly infer emotional causes, enabling causal reasoning between historical and target sentences. Additionally, we enhance sentiment classification by leveraging large language models (LLMs) with dynamic example retrieval, constructing an experience database to guide the model using contextually relevant instances. To further improve adaptability, we design a semantic interpretation task for refining emotion category representations and fine-tune the LLM accordingly. Experiments on three benchmark datasets show that our approach significantly improves accuracy and reliability, surpassing traditional deep-learning methods. These findings underscore the effectiveness of structured reasoning, knowledge retrieval, and LLM-driven sentiment adaptation in advancing emotion–cause-based sentiment analysis.