Emotion Traceability Analysis: A Multi Strategy Framework for LLM Dialogue Processing

Recent approaches in emotional causal reasoning have leveraged Retrieval-Augmented Generation RAG and multimodal fusion to enhance the accuracy of large language models LLMs in analyzing emotions. As a critical cognitive process for understanding, inferring and predicting the antecedents and consequences of emotional states, emotional causal reasoning primarily involves two components: emotional understanding and causal inference. However, LLMs face two key challenges in analyzing emotional causality: 1 the inability to process ultra-long texts due to input length constraints, and 2 insufficient capability to track emotional dynamics in dialogues. To address these limitations, we propose the Emotion Traceability Analysis Framework ETAF , which employs RAG-based keyword retrieval to extract critical events from dialogues and dynamically segments conversations according to event progression, enabling LLMs to comprehend contextualized events holistically. In addition, we integrate character analysis and variation correction modules to improve the precision of the model in tracking emotional causal chains between characters and refining the interpretation of emotional shifts. Experimental results on the ATLAS-6 dataset demonstrate that our framework improves the performance of GLM-4-air by 17.79 , outperforming DeepSeek-R1 origin by 6.49 and achieving state-of-the-art results.