识别与痴呆症和COVID-19相关的X (以前的Twitter) 帖子:机器学习方法
Mehrnoosh Azizi1, Ali Akbar Jamali1, Raymond J Spiteri1
1Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
JMIR formative research
|June 4, 2024
概括
转移学习算法,如ALBERT,有效地识别社交媒体上的COVID-19和痴呆症帖子. 这种自动化有助于研究人员,并通过减少手动数据分析来支持弱势群体.
科学领域:
- 计算语言学计算语言学
- 医疗保健中的人工智能
- 社交媒体分析.
背景情况:
- 痴呆症患者是流行病期间脆弱的人群.
- 像X (以前的Twitter) 这样的社交媒体平台是COVID-19更新的关键信息来源.
- 识别与痴呆相关的职位对于支持患者和护理人员至关重要,但由于数据量而具有挑战性.
研究的目的:
- 为了自动识别与痴呆症和COVID-19相关的社交媒体帖子.
- 为了实现这一任务,利用自然语言处理和机器学习.
主要方法:
- 利用自然语言处理和机器学习算法.
- 雇员手动注释职位用于培训和验证.
- 在三个数据集中评估了超过10万个帖子的算法性能.
主要成果:
- 转移学习算法显著超过了传统的机器学习方法.
- 阿尔伯特 (A Lite变压器双向编码器表示) 模型实现了82.92%的准确性和83.53%的AUC.
- 阿尔伯特在分类相关职位方面表现出色.
结论:
- 转移学习模型,如ALBERT,对于特定主题的社交媒体后识别非常有效.
- 这些算法甚至在有限或相邻的数据中表现出色,优于其他机器学习方法.
- 自动化分析减少了手工编码工作量,帮助研究人员和决策者支持弱势群体.
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