关于美国菜单标签法规的推特的情绪分析
Yuyi Yang1,2, Nan Lin3, Quinlan Batcheller2
1Division of Computational and Data Science, Washington University, St. Louis, MO 63130, USA.
Nutrients
|October 14, 2023
概括
机器学习分析了使用Twitter数据对美国菜单标签法规的公众意见. 虽然最初是中立的或以新闻为重点的,情绪分析显示,最近的负面观点有所增加,突出了社交媒体的负面观点.
科学领域:
- 公共卫生 公共卫生
- 计算社会科学 计算社会科学
- 卫生政策 卫生政策
背景情况:
- 美国菜单标签法规要求连锁餐馆显示标准菜单项的卡路里信息.
- 这些规定旨在促进更健康的饮食选择和打击肥胖.
- 了解公众的看法对于有效的政策实施和改进至关重要.
研究的目的:
- 为了对美国菜单标签法规的公众意见进行一种新的情绪分析.
- 通过使用Twitter数据,从2008年到2022年跟踪公众对这些法规的情绪演变.
- 确定影响公众对菜单标签的情绪的关键因素.
主要方法:
- 利用机器学习技术,包括深度神经网络 (RoBERTa) 和随机森林模型,进行情绪分析.
- 从2008年到2022年收集并系统搜索的推特数据,标注推特为积极,消极,中立或新闻.
- 进行时间分析,观察随时间推移的论趋势,并确定情绪的关键预测因素.
主要成果:
- 推特的数量在重大政策公告时达到顶峰,大多数推特被归类为中立或与新闻相关的.
- 2017年以后,与新闻相关的推特的比例下降,中立观点变得越来越普遍.
- 罗伯塔在情绪分类中实现了92%的准确性;作者的追随者和推特活动是关键的情绪预测指标,最近观察到负面情绪的上升.
结论:
- 社交媒体,特别是推特,是持续监测公众对卫生政策态度的宝贵工具.
- 该研究为公众对菜单标签法规认知的动态演变提供了独特的见解.
- 通过了解公众情绪的变化,研究结果可以为未来的卫生政策制定,执行和改进提供信息.
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