使用推特数据分析患者对医疗服务的满意度
Muhammad Usman1, Muhammad Mujahid1, Furqan Rustam2
1Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
PeerJ. Computer science
|January 23, 2024
概括
根据对推特数据的分析,公众对医疗保健服务的情绪主要是积极的. 一个新的转移学习模型在情绪分类中实现了0.95准确度,超过了传统的机器学习方法,提高了医疗保健质量.
科学领域:
- 医疗信息学 医疗信息学
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
背景情况:
- 由于COVID-19,公众对卫生系统的担忧急剧增加.
- 像Twitter这样的社交媒体平台对于医疗保健的实时公众论至关重要.
- 现有使用机器学习 (ML) 和深度学习 (DL) 进行医疗保健推文情绪分析的研究表明其准确性和验证性有限.
研究的目的:
- 通过使用Twitter数据来确定全球公众对医疗服务的情绪.
- 评估各种ML和DL模型对医疗保健相关推特的情感分类的有效性.
- 引入和验证一种基于转移学习的新型模型,用于增强情绪分析.
主要方法:
- 采集了使用标签#医疗保健,#医疗保健服务和#医疗设施的推文.
- 进行了情绪分析,考虑了位置和推特内容.
- 评估了多个ML模型 (SVM,物流回归,天真贝叶斯,ETC,KNN,随机森林,决策树,AdaBoost) 和一个拟议的LSTM-ETC模型.
主要成果:
- 额外树分类器 (ETC) 模型实现了0.88.8的准确性.
- 提出的基于转移学习的LSTM-ETC模型表现出卓越的性能,准确度为0.95.
- 分析显示,公众对医疗保健服务的情绪主要是积极的,比负面的推文要积极得多.
结论:
- 拟议的LSTM-ETC模型为医疗保健推特情绪分类提供了一个高度有效的方法.
- 社交媒体,特别是推特上的公众反为改善医疗保健质量提供了宝贵的见解.
- 积极的公众情绪表明总体满意度,这对于医疗保健的战略决策至关重要.
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