Identifying the targets of emotional words and phrases in crisis situations, particularly health-related crises, is essential 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 large language model generates aspect-level labels and reasoning traces to supervise a smaller student model across five tasks: 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 annotations, along with the annotation pipeline and fine-tuned student models as open-source resources. The resulting student model supports end-to-end aspect-based sentiment analysis and emotion detection at substantially lower inference cost than the teacher, making the approach suitable for practitioners with limited access to annotated data and large-scale computational resources. Because CrisisKD requires only raw text and a dependency parser, the framework is architecturally portable to other crisis domains and to languages supported by suitable multilingual parsers.