使用多种功能工程技术对COVID-19疫苗接种推特进行时间分析和意见动态
Shoaib Ahmed1, Dost Muhammad Khan1, Saima Sadiq2
1Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
PeerJ. Computer science
|June 22, 2023
概括
这项研究使用情绪分析分析了社交媒体上的COVID-19疫苗接种意见. 额外的树木分类器与字袋实现了92%的准确性,显示了随着时间的推移增加的支持疫苗的情绪.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生信息学 公共卫生信息学
- 自然语言处理自然语言处理.
背景情况:
- 社交媒体平台成为COVID-19信息和公共话语的主要来源.
- 关于COVID-19疫苗接种的错误信息和阴谋论广泛传播,影响了公众的看法.
- 了解论动态对于有效的公共卫生政策和疫苗接种活动至关重要.
研究的目的:
- 通过使用社交媒体数据,探索有关COVID-19疫苗接种的论动态.
- 开发和评估针对COVID-19疫苗接种相关推文的情绪分析框架.
- 为公共卫生当局制定提高接种疫苗接受率的策略提供信息.
主要方法:
- 为处理COVID-19疫苗接种推文提出了一种情绪分析框架.
- 探索了各种特征提取技术,包括术语频率-反向文档频率 (TF-IDF),词包 (BoW),Word2Vec和TF-IDF与BoW.
- 评估了诸如随机森林,梯度增强机,额外树分类器 (ETC),物流回归,天真贝叶斯,随机梯度下降,多层感知器,卷积神经网络 (CNN),变压器的双向编码器表示 (BERT),长期短期记忆 (LSTM) 和循环神经网络 (RNN) 等分类器.
主要成果:
- 额外树分类器 (ETC) 模型,利用字袋 (BoW) 功能提取方法,实现了92%的最高准确度.
- 这种组合在分析COVID-19疫苗接种相关推文情绪方面是最有效的.
- 对民意动态的分析表明,对疫苗接种的积极情绪在一段时间内有所增加.
结论:
- 提出的情绪分析框架,特别是使用ETC与BoW,是了解公众对COVID-19疫苗接种的意见的合适方法.
- 这些发现表明,随着时间的推移,人们越来越接受COVID-19疫苗接种,这反映在社交媒体话语中.
- 这项研究为公共卫生官员提供了有价值的见解,旨在改善疫苗接种策略和打击错误信息.
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