与显式情感分析不同的是，隐式情感分析主要是针对那些不显式包含情感极性却在语境中明确表达了情感观点的句子进行的深层次隐含语义分析，其在细粒度情感分析中被广泛应用。目前已有工作主要通过预训练语言模型来获取文本表示并通过微调推理情感语义信息，但对于文本深层次的隐含语义推理分析仍有不足，容易产生“幻觉”现象。该文提出一种基于教师-学生模型的隐式情感推理框架(reasoning implicit sentiment with teacher-student prompting, RI-TSP)，其中教师模型采用大规模语言模型T5，运用零样本思维链提示构建推理样本，并利用知识蒸馏、迁移学习等技术使训练小模型具备一定的语义分析和多跳推理能力。实验结果表明，在Laptops和Restaurants这2个公开数据集上，该文提出的RI-TSP方法与基于Prompt的模型相比，在隐式情感推理分析中的准确率分别提升了1.73%和3.49%，F1值分别提升了2.30%和1.46%，训练效率分别提升了25.0%和50.0%。

Objective: Sentiment analysis is an important subtask of natural language processing. Its core is to mine the sentiment aspects and sentiment polarity pairs in sentences and conduct relevant analysis. It is mainly divided into explicit sentiment analysis (ESA) and implicit sentiment analysis (ISA). Unlike the former, the latter mainly analyzes the implicit semantics of sentences that do not explicitly contain emotional polarity but clearly express emotional views in the context. ISA is widely used in fine-grained sentiment analysis. Currently, ESA dominates the sentiment analysis tasks, and its comment sentences explicitly contain sentiment partial words and sentiment polarity. In the existing work, text representations are mainly obtained through pretrained language models and emotional semantic information is inferred through fine-tuning; however, the deeper implicit semantic reasoning analysis of text still has shortcomings, and the fusion produces an "illusion." Methods: Therefore, this study first proposes a framework for implicit sentiment reasoning based on teacher-student prompting (reasoning implicit sentiment with teacher-student prompting, RI-TSP). Through the reasoning of three-layer thought chains, the implicit emotional information in the sentence can be more effectively mined. Second, a prompt fine-tuning paradigm was designed from the teacher to the student model. In this paradigm, the teacher model uses the zero-shot method to generate reasoning samples, and then, the student model performs prompt fine-tuning and training. Finally, the fine-tuning training of the large model is transferred to the small-scale model through the knowledge distillation method. Results: Experimental results show that on the Laptops and Restaurants datasets, the proposed RI-TSP method outperformed state-of-the-art methods, improving implicit sentiment inference accuracy by 1.73% and 3.49%, F1 value by 2.30% and 1.46%, and training efficiency by 25.0% and 50.0%, respectively. For these two datasets, the RI-TSP model achieved a higher improvement in ESA compared with RGAT, BERT+SPC, BERT+ADA, BERT+RGAT, and prompt-based models. Conclusions: Using a large-scale teacher language model, a sentiment polarity prompt method was developed to generate reasoning samples. Using the knowledge of thought chain and prompt learning, samples with thought chain reasoning processes were effectively generated. In addition, through knowledge distillation, samples with reasoning knowledge were fine-tuned on a small-scale student model to achieve sentiment polarity reasoning. Experimental results on the public datasets Laptops and Restaurants showed that the RI-TSP model had a high accuracy rate and low running cost.