SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment
This addresses fine-grained emotion classification for multilingual complex text analysis, though it appears incremental as it builds on existing LLM capabilities with specific enhancements.
The authors tackled fine-grained emotion classification in multilingual complex text environments by proposing SentiXRL, a framework with emotion retrieval enhancement and self-circulating analysis modules, which outperformed existing models on datasets like CPED and CH-SIMS and showed overall better performance on MELD, Emorynlp, and IEMOCAP.
With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification across multi-lingual and complex contexts. To address this, we propose the Sentiment Cross-Lingual Recognition and Logic Framework (SentiXRL), which incorporates two modules,an emotion retrieval enhancement module to improve sentiment classification accuracy in complex contexts through historical dialogue and logical reasoning,and a self-circulating analysis negotiation mechanism (SANM)to facilitates autonomous decision-making within a single model for classification tasks.We have validated SentiXRL's superiority on multiple standard datasets, outperforming existing models on CPED and CH-SIMS,and achieving overall better performance on MELD,Emorynlp and IEMOCAP. Notably, we unified labels across several fine-grained sentiment annotation datasets and conducted category confusion experiments, revealing challenges and impacts of class imbalance in standard datasets.