EMSA:可解释的多语言情绪分析模型,提供跨多种语言的情绪分析
Li Zhao1, Jinwei Zhou2, Jinde Cao3
1School of Information Science and Engineering, Yunnan University, Kunming, China.
PloS one
|November 12, 2025
概括
本研究介绍了可解释的多语言情绪分析仪 (EMSA),用于改进跨语言情绪分析. 欧洲电气安全局 (EMSA) 提高了模型的解释性,并且在中国和英语数据集上优于现有的方法.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 多语言情绪分析面临语言多样性和缺乏模型可解释性的挑战.
- 现有的模型往往缺乏透明度,阻碍了用户的信任和实际应用.
研究的目的:
- 开发一种可解释的多语言情绪分析仪 (EMSA),以解决跨语言情绪分析的局限性.
- 提高不同语言情绪分析模型的可解释性和性能.
主要方法:
- 提出了一个新的框架,EMSA,将大型语言模型与快速工程结合起来.
- 采用了两阶段的过程:思维链促使推理和分类的明确可解释性.
- 在GubaSenti (中国金融) 和SST (英语基准) 数据集上对EMSA进行评估.
主要成果:
- 与RoBERTa,XLNet和ALBERT等预训练模型相比,EMSA表现出更高的性能.
- 该框架提供了透明的推理步骤,增加了用户的信任.
- 实现了提高多语言情感分类准确性和可解释性.
结论:
- 通过将高性能与可解释性相结合,EMSA在多语言情绪分析方面取得了重大进展.
- 拟议的框架有助于开发更可靠和实用的情绪分析系统.
- 突出了用于可解释AI的大型语言模型的快速工程的潜力.
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