  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.
