在西班牙语中使用语言特征和变换器进行细粒度情绪分析
Alejandro Salmerón-Ríos1, José Antonio García-Díaz1, Ronghao Pan1
1Departamento de Informática y Sistemas, Universidad de Murcia, Campus de Espinardo, Murcia, Murcia, Spain.
PeerJ. Computer science
|June 10, 2024
概括
这项研究引入了一个新的西班牙情感数据集,用于在社交媒体上分析心理健康. 玛丽亚模型取得了最佳表现,突出了AI在检测抑郁信号方面的潜力.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 社交媒体分析.
背景情况:
- 心理健康问题,特别是抑郁症,是全球日益关注的问题.
- 社交媒体平台越来越多地用于分享健康信息和寻求支持.
- 在线表达的情绪和心理健康状况之间存在联系,这为早期检测提供了潜力.
研究的目的:
- 以西班牙语探索情绪分析,以使用社交媒体数据检测心理健康障碍.
- 创建和评估一个新的,全面的西班牙情感数据集.
- 在本数据集上评估最先进的变压器模型的性能.
主要方法:
- 编译,翻译和评估一个新型数据集,包含16种情绪,重点关注负面情绪.
- 基于变压器的模型 (仅编码器和编码器解码器) 的深入评估.
- 分析包括单语言,多语言和蒸模型,以及功能集成技术.
主要成果:
- 只有编码器的玛丽亚型号实现了最高的性能.
- 使用MARIA模型获得了60.4771%的宏观平均F1得分.
- 这项研究证明了特定的变压器架构对于西班牙语情绪分析在心理健康环境中的有效性.
结论:
- 对社交媒体数据的自动化情绪分析对早期发现抑郁症等心理健康问题充满希望.
- 开发的数据集和评估的模型为该领域的研究提供了宝贵的资源.
- 人工智能的进一步进步可以帮助通过社交媒体监测和支持公共心理健康.
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