Implicit Sentiment Analysis (ISA) is a crucial research area in natural language processing. Inspired by the idea of large language model Chain of Thought (CoT), this paper introduces a Sentiment Analysis of Thinking (SAoT) framework. The framework first analyzes the implicit aspects and opinions in the text using common sense and thinking chain capabilities. Then, it reflects on the process of implicit sentiment analysis and finally deduces the polarity of sentiment. The model is evaluated on the SemEval 2014 dataset, consisting of 1120 restaurant reviews and 638 laptop reviews. The experimental results demonstrate that the utilization of the ERNIE-Bot-4+SAoT model yields a notable performance improvement. Specifically, on the restaurant dataset, the F1 score reaches 75.27, accompanied by an ISA score of 66.29. Similarly, on the computer dataset, the F1 score achieves 76.50, while the ISA score amounts to 73.46. Comparatively, the ERNIE-Bot-4+SAoT model surpasses the BERTAsp + SCAPt baseline by an average margin of 47.99%.

翻译：隐式情感分析是自然语言处理领域的重要研究方向。受大语言模型思维链思想的启发，本文提出了一种思维情感分析框架。该框架首先利用常识与思维链能力分析文本中的隐式方面与观点，进而对隐式情感分析过程进行反思，最终推导出情感极性。模型在SemEval 2014数据集上进行了评估，该数据集包含1120条餐厅评论和638条笔记本电脑评论。实验结果表明，采用ERNIE-Bot-4+SAoT模型可带来显著的性能提升。具体而言，在餐厅数据集上，F1分数达到75.27，同时ISA分数为66.29；在电脑数据集上，F1分数达到76.50，ISA分数为73.46。相比BERTAsp + SCAPt基线模型，ERNIE-Bot-4+SAoT模型的性能平均提升幅度达47.99%。