Fine-grained sentiment analysis involves extracting and organizing sentiment elements from textual data. However, existing approaches often overlook issues of category semantic inclusion and overlap, as well as inherent structural patterns within the target sequence. This study introduces a generative sentiment analysis model. To address the challenges related to category semantic inclusion and overlap, a latent category distribution variable is introduced. By reconstructing the input of a variational autoencoder, the model learns the intensity of the relationship between categories and text, thereby improving sequence generation. Additionally, a trie data structure and constrained decoding strategy are utilized to exploit structural patterns, which in turn reduces the search space and regularizes the generation process. Experimental results on the Restaurant-ACOS and Laptop-ACOS datasets demonstrate a significant performance improvement compared to baseline models. Ablation experiments further confirm the effectiveness of latent category distribution and constrained decoding strategy.

翻译：细粒度情感分析涉及从文本数据中提取并组织情感要素。然而，现有方法常忽视类别语义包含与重叠问题，以及目标序列内部固有的结构模式。本研究提出了一种生成式情感分析模型。为应对与类别语义包含和重叠相关的挑战，模型引入了潜在类别分布变量。通过重构变分自编码器的输入，模型学习类别与文本之间关系的强度，从而改善序列生成。此外，模型利用字典树数据结构与约束解码策略来挖掘结构模式，这反过来缩小了搜索空间并规范了生成过程。在Restaurant-ACOS和Laptop-ACOS数据集上的实验结果表明，相较于基线模型，本模型取得了显著的性能提升。消融实验进一步证实了潜在类别分布与约束解码策略的有效性。