用西班牙语评估金融定向情绪分析的变压器模型
Ronghao Pan1, José Antonio García-Díaz1, Francisco Garcia-Sanchez1
1Faculdad de Informática, Universidad de Murcia, Murcia, Spain.
PeerJ. Computer science
|June 22, 2023
概括
本研究引入了针对金融文本的定向情绪分析,这对于股票市场预测至关重要. 它强调了西班牙大语言模型在分析对经济目标,社会和公司的情绪方面的有效性.
科学领域:
- 自然语言处理自然语言处理.
- 计算金融是指计算金融.
- 情绪分析 情绪分析
背景情况:
- 来自社交媒体的财务数据对于股票市场预测至关重要.
- 越来越多的财务信息量和各种情绪需要自动化分析工具.
- 现有的方法很难有效地对各种利益相关者的情绪进行分类.
研究的目的:
- 开发和评估针对金融文本的有针对性的情绪分析系统.
- 自动提取经济目标并对它们,其他公司和社会进行分类.
- 创建一个新的西班牙研究金融机构.
主要方法:
- 在西班牙语汇编了新的财务推特和新闻标题集.
- 针对西班牙语的各种大型语言模型 (LLM) 的性能比较.
- 评估用于情绪分析的多标签分类模型.
主要成果:
- 西班牙的LLMs,MARIA和BETO,在情绪分类中表现强.
- 实现的宏观F1分数为76.04% (目标),74.16% (社会) 和68.07% (公司).
- 目标检测准确率达到69.74%,多标签分类的表现为71.13%.
结论:
- 有针对性的情绪分析是有效的理解金融文本的两极性.
- 西班牙语LLM提供了一个有前途的解决方案,用于分析西班牙语的金融情绪.
- 开发的语料库是西班牙语研究社区的宝贵资源.
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