用分步提示与模型集成，精准分析多轮对话中的情感要素与变化。
Structured Prompting and LLM Ensembling for Multimodal Conversational Aspect-based Sentiment Analysis
- 分步引导大模型逐项提取情感六元组，提升上下文理解。
- 三模型集成实现74.12%的精确匹配F1，有效识别情感转折。
- 适合研究多模态情感分析、对话系统的情感建模者。
多模态对话中的情感理解是构建情感智能AI系统的关键挑战。多模态对话方面情感分析（MCABSA）挑战要求完成两个高难度子任务：（1）从多说话人对话中提取包含持有者、目标、方面、观点、情感和理由的完整情感六元组；（2）检测情感翻转，即动态情感变化及其触发因素。针对子任务一，本文设计了结构化提示流程，引导大语言模型（LLMs）以序列方式提取情感成分并增强上下文理解。针对子任务二，通过集成三个LLM的互补优势，稳健识别情感转变及其触发因素。系统在子任务一上获得47.38%平均得分，在子任务二上达到74.12%精确匹配F1，验证了逐步优化与集成策略在复杂多模态情感分析中的有效性。
原文摘要 · Abstract (English)
Understanding sentiment in multimodal conversations is a complex yet crucial challenge toward building emotionally intelligent AI systems. The Multimodal Conversational Aspect-based Sentiment Analysis (MCABSA) Challenge invited participants to tackle two demanding subtasks: (1) extracting a comprehensive sentiment sextuple, including holder, target, aspect, opinion, sentiment, and rationale from multi-speaker dialogues, and (2) detecting sentiment flipping, which detects dynamic sentiment shifts and their underlying triggers. For Subtask-I, in the present paper, we designed a structured prompting pipeline that guided large language models (LLMs) to sequentially extract sentiment components with refined contextual understanding. For Subtask-II, we further leveraged the complementary strengths of three LLMs through ensembling to robustly identify sentiment transitions and their triggers. Our system achieved a 47.38% average score on Subtask-I and a 74.12% exact match F1 on Subtask-II, showing the effectiveness of step-wise refinement and ensemble strategies in rich, multimodal sentiment analysis tasks.
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