使用深度学习来分析COVID-19的心理影响
Monira Abdulrahman Almeqren1, Latifah Almuqren2, Fatimah Alhayan2
1Education College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Frontiers in psychology
|September 4, 2023
概括
这项研究开发了一种用于阿拉伯情绪分析的深度学习模型,以预测沙特阿拉伯COVID-19期间的焦虑水平. 该模型实现了88%的准确性,在社交媒体用户中确定了低焦虑率.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 心理学 心理学 心理学
背景情况:
- 情感分析 (SA) 使用NLP自动化态度分类.
- 深度学习在SA方面表现有前途,但阿拉伯语SA由于语言复杂性和数据稀缺性而面临挑战.
- 在阿拉伯研究中,焦虑作为一种心理情绪是未被充分探索的.
研究的目的:
- 为了应对阿拉伯情绪分析 (ASA) 的挑战.
- 在沙特阿拉伯开发一种深度学习模型,用于预测COVID-19期间的焦虑水平.
主要方法:
- 验证了一种心理尺度来衡量焦虑.
- 创建了阿拉伯心理学词典 (AraPh),其中包含138个方言阿拉伯焦虑词.
- 使用Bi-GRU和字嵌入的955条阿拉伯语推文的语料库 (焦虑) 进行了注释.
主要成果:
- 该SA模型实现了88%的准确性,89%的精度,88%的回忆和87%的F1得分.
- 77%的推文显示没有焦虑,17%轻度焦虑,6%高焦虑.
- 该模型有效地分析了情绪,并确定了推特中的焦虑水平.
结论:
- 拟议的模型可以帮助沙特卫生部提高心理性.
- 研究结果提供了关于沙特阿拉伯COVID-19大流行期间公众焦虑程度的见解.
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