分析情绪变化检测Covid-19推文的检测
Panagiotis C Theocharopoulos1, Anastasia Tsoukala1, Spiros V Georgakopoulos2
1Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
Neural computing & applications
|June 26, 2023
概括
使用自然语言处理和机器学习进行社交媒体情绪分析,可以了解COVID-19大流行期间公众的态度. 这项研究确定了公众观念的关键变化及其与新闻报道的相关性.
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:
- 由于Covid-19的流行,需要封锁,减少面对面的互动,增加社交媒体的使用.
- 在疫情期间,像Twitter这样的社交媒体平台对于衡量公众情绪至关重要.
- 在工业4.0框架内,情绪分析对于了解公众对危机的态度是有价值的.
研究的目的:
- 开发和评估一个分析框架来分类关于Covid-19大流行的推文情绪 (积极/消极).
- 在整个大流行期间对公众情绪趋势进行时间序列分析.
- 确定公众态度的关键转折点及其与新闻报道的关系.
主要方法:
- 应用自然语言处理 (NLP) 技术和机器学习算法来对推特情绪进行分类.
- 进行广泛的实验,以确定最佳的情绪分析模型.
- 使用变化点检测算法来识别公众情绪的变化.
- 交叉引用确定了与当代新闻报道进行验证的转折点.
主要成果:
- 确定最有效的机器学习模型,用于对与大流行相关的推文进行情绪分析.
- 时间序列分析的演示,揭示了Covid-19大流行期间公众情绪的演变.
- 确定公众态度的重大变化点,根据新闻事件进行验证.
- 分析社交媒体情绪趋势与新闻报道之间的相关性.
结论:
- 开发的框架有效地分析了全球卫生危机期间在社交媒体上的公众情绪.
- 情绪分析和变化点检测为公众认知转变及其驱动因素提供了宝贵的见解.
- 了解社交媒体情绪和新闻报道之间的相互作用对于公共卫生沟通和政策至关重要.
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