用大模型拆解事件，提升多模态情感分析精度
Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis
- 用大模型将文本分解为多个事件，降低分析复杂度
- 在两个基准数据集上优于现有先进方法
- 适合研究多主体多情感场景下的情感分析
随着互联网快速发展，用户生成内容日益丰富，多模态方面级情感分析（MABSA）成为研究热点。现有研究在MABSA上取得一定成果，但在多个实体与情感共存的场景中仍面临分析挑战。本文创新性地引入大语言模型（LLMs）进行事件分解，并提出一种基于强化学习的多模态方面级情感分析框架（MABSA-RL）。该框架利用LLMs将原始文本分解为一组事件，降低分析复杂度，并引入强化学习优化模型参数。实验结果表明，MABSA-RL在两个基准数据集上均优于现有先进方法。本文为多模态方面级情感分析提供了新的研究视角与方法。
原文摘要 · Abstract (English)
With the rapid development of the internet, the richness of User-Generated Contentcontinues to increase, making Multimodal Aspect-Based Sentiment Analysis (MABSA) a research hotspot. Existing studies have achieved certain results in MABSA, but they have not effectively addressed the analytical challenges in scenarios where multiple entities and sentiments coexist. This paper innovatively introduces Large Language Models (LLMs) for event decomposition and proposes a reinforcement learning framework for Multimodal Aspect-based Sentiment Analysis (MABSA-RL) framework. This framework decomposes the original text into a set of events using LLMs, reducing the complexity of analysis, introducing reinforcement learning to optimize model parameters. Experimental results show that MABSA-RL outperforms existing advanced methods on two benchmark datasets. This paper provides a new research perspective and method for multimodal aspect-level sentiment analysis.
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