CrisisKD：危机话语中方面级情感与情绪分析的五阶段知识蒸馏
CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse
- University of Twente（特文特大学）
- University of Zagreb（萨格勒布大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出CrisisKD五阶段知识蒸馏框架，用教师LLM生成标签和推理，训练学生模型进行方面级情感与情绪分析，显著提升性能并降低推理成本。
AI中文摘要：
识别危机情境（尤其是健康相关情境）中情绪词或短语的目标，对于理解不同文化和语言背景下的公众关切至关重要。我们提出CrisisKD，一个用于未标注社交媒体数据方面级情感与情绪分析的五阶段师生知识蒸馏框架。教师大语言模型生成方面级标签和推理轨迹，用于监督较小的学生模型完成方面提取、句法解析、观点提取、情感分类和情绪分类任务。利用该框架，我们构建并发布了一个包含50,615个方面级标签的数据集，以及标注和微调脚本作为开源资源。所得到的学生模型支持端到端的ABSA和情绪检测，推理成本显著低于教师模型。在人工标注的500条推文黄金集上，5任务Qwen2.5-7B学生模型相比未调优模型，在方面提取上F1值提升7.9点，情绪准确率提升17.0点，情感准确率提升6.5点。在外部ABEA基准上，CrisisKD将同模型Qwen2.5-7B的ICL基线在ATE上提升2.8个F1点，在联合ATE+AEC上提升3.8个F1点。
英文摘要：
Identifying the target of emotional words or phrases in crisis situations, especially health-related ones, is important for understanding public concerns across cultural and linguistic contexts. We propose CrisisKD, a five-stage teacher--student knowledge distillation framework for aspect-level sentiment and emotion analysis on unannotated social media data. A teacher LLM generates aspect-level labels and reasoning traces that supervise a smaller student model across aspect extraction, syntactic parsing, opinion extraction, sentiment classification, and emotion classification. Using this framework, we construct and release a dataset containing 50,615 aspect-level labels, together with the annotation and fine-tuning scripts as open-source resources. The resulting student supports end-to-end ABSA and emotion detection at substantially lower inference cost than the teacher. On a manually annotated 500-tweet gold set, the 5-task Qwen2.5-7B student improves over the untuned model by 7.9 F1 points on aspect extraction, 17.0 points on emotion accuracy, and 6.5 points on sentiment accuracy. On the external ABEA benchmark, CrisisKD improves the same-model Qwen2.5-7B ICL baseline by 2.8 F1 points on ATE and 3.8 F1 points on joint ATE+AEC.
