[目的/意义] 针对传统情感分析方法在突发企业舆情场景中标注依赖重、隐晦情感识别弱、泛化能力不足的核心局限，探究生成式人工智能在少样本条件下实现企业舆情情感识别与声誉风险细分的可行性，为突发舆情监测预警提供方法支持。 [方法/过程] 以“始祖鸟喜马拉雅烟花事件”微博评论为实证数据，构建融合提示工程与上下文学习的单次推理—双任务输出生成式人工智能情感分析模型；采用双流程少样本示例构建方案，对结构化提示词与通用提示词开展严格对照实验，引入传统机器学习与深度学习基线模型进行横向对比，并通过双示例集稳健性检验验证实验可靠性。 [结果/结论] 三种生成式人工智能模型在本实验中整体性能均优于传统基线模型，其中Doubao-Seed-1.6在结构化提示词条件下整体准确率达84.22%，负向情感召回率为90.71%；结构化提示词在整体准确率与负向情感识别上均优于通用提示词，可在完成情感三分类的基础上同步输出四类核心及其他声誉风险标签，实验结果具备良好的可重复性与鲁棒性。该方法可在低标注依赖下实现情感分类与风险细分，为企业突发舆情的分级预警与精准危机响应提供数据支撑。

[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.