[Purpose/Significance] Targeting the core limitations of traditional sentiment analysis methods in emergency corporate public opinion scenarios—heavy reliance on annotated data, weak implicit sentiment recognition, and insufficient generalization—this paper examines the feasibility of Generative Artificial Intelligence (GAI) in achieving sentiment recognition and reputation risk segmentation under few-shot learning conditions, aiming to provide methodological support for emergency public opinion monitoring and early warning. [Method/Process] Using Weibo comments from the "Arc'teryx Himalayan Fireworks Incident" as empirical data, this paper constructed a single-inference dual-task output GAI sentiment analysis model integrating prompt engineering and in-context learning. A dual-process few-shot example construction scheme was adopted, and strictly controlled experiments were conducted between structured prompts and general prompts. Traditional machine learning and deep learning baseline models were incorporated for horizontal comparison, and reliability was further validated through dual example-set robustness testing. [Result/Conclusion] All three GAI models outperform the traditional baseline models in this experiment. Doubao-Seed-1.6 achieves an overall accuracy of 84.22% with a negative sentiment recall of 90.71% under structured prompts. Structured prompts outperform general prompts in both overall accuracy and negative sentiment recognition, while simultaneously outputting four core and other reputation risk labels alongside the three-class sentiment classification. Experimental results demonstrate good repeatability and robustness. This method enables sentiment classification and risk segmentation with low reliance on labeled data, providing structured data support for tiered early warning and targeted crisis response in emergency corporate public opinion management.