SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment

TL;DR

SentiXRL is a novel multilingual framework that enhances fine-grained emotion classification in complex texts by integrating logical reasoning and dialogue history, outperforming existing models across multiple datasets.

Contribution

The paper introduces SentiXRL, a new framework combining emotion retrieval and autonomous decision-making modules for improved multilingual sentiment analysis.

Findings

Outperforms existing models on CPED and CH-SIMS datasets.

Achieves superior results on MELD, Emorynlp, and IEMOCAP.

Reveals challenges of class imbalance in sentiment datasets.

Abstract

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.