多语言细粒度情感分类新框架，提升复杂语境下情绪识别准确率
SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment
- 引入历史对话与逻辑推理增强情绪检索，提升复杂语境理解
- 自循环分析协商机制使模型自主决策，性能优于现有模型
- 统一多数据集标签，揭示类别不平衡对分类的影响
大型语言模型虽具备强大的表达能力，能有效捕捉情感结构与深层语义，但在多语言及复杂语境下的细粒度情感分类仍面临挑战。为此，我们提出情感跨语言识别与逻辑框架（SentiXRL），包含两个模块：情绪检索增强模块，通过历史对话与逻辑推理提升复杂语境下的分类准确率；自循环分析协商机制（SANM），实现单模型内自主决策。在多个标准数据集上验证表明，SentiXRL在CPED和CH-SIMS上超越现有模型，在MELD、Emorynlp和IEMOCAP上整体表现更优。特别地，我们统一了多个细粒度情感标注数据集的标签，并开展类别混淆实验，揭示了标准数据集中类别不平衡带来的挑战与影响。
原文摘要 · Abstract (English)
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.
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