Multi-Step Cross-Domain Aspect-Based Sentiment Generation with Error Correction Mechanism

With the rapid growth of social media and user-generated content, cross-domain aspect-level sentiment analysis has become an important research direction in sentiment computing. In this study, a cross-domain sentiment analysis method based on the T5 model is proposed. This method integrates a multi-step generative training mechanism with a correction mechanism to improve the model's generalization ability and sentiment classification accuracy when processing texts from different domains. First, domain-invariant sentiment features are extracted through training on texts and their associated aspect vocabularies from both the source and target domains. This process effectively reduces inter-domain discrepancies. Unlike other methods, the generative task is formulated in the source domain to produce both aspect and sentiment element pairs, which improves the model's reasoning ability through multi-step generation. Finally, a correction mechanism is used to detect the aspect labels in the generated labels of the target domain and regenerate the sentiment predictions when errors are detected, which improves the model’s robustness. Experimental results show that the proposed method performs well in several cross-domain sentiment analysis tasks and significantly outperforms traditional methods in sentiment classification accuracy. The study provides an innovative solution for cross-domain sentiment analysis with broad application potential.