用五阶段知识蒸馏，让小模型高效分析危机文本中的情感与观点。
CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse
- 分五步蒸馏大模型的推理过程，指导小模型完成多任务分析。
- 在500条人工标注数据上，小模型情感准确率提升17.0点，句法解析提升7.9点。
- 适合需要低成本部署情感分析系统的应急响应、公共舆情监控场景。
在危机情境（尤其是健康相关）中识别情绪词或短语的目标对象，对于理解跨文化、跨语言的公众关切至关重要。本文提出 CrisisKD，一种五阶段教师-学生知识蒸馏框架，用于在未标注社交媒体数据上进行细粒度情感与情绪分析。教师大模型生成细粒度标签和推理路径，监督学生模型完成方面提取、句法解析、观点抽取、情感分类和情绪分类五个任务。基于该框架，我们构建并开源了一个包含50,615个细粒度标签的数据集，以及标注与微调脚本。最终学生模型实现了端到端的ABSA与情绪检测，推理成本远低于教师模型。在500条人工标注的黄金数据集上，5任务的Qwen2.5-7B学生模型相较未调优模型，在方面提取上提升7.9 F1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基准测试中，相比同一模型的ICL基线，提升了2.8 F1点（ATE）和3.8 F1点（联合ATE+AEC）。
原文摘要 · Abstract (English)
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.
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