基于使用机器学习技术的COP9推特,对烟草控制的话题预测
Sherif Elmitwalli1, John Mehegan1, Georgie Wellock1
1Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom.
这项研究使用机器学习准确地预测了在线讨论烟草减少危害的讨论,达到91.87%的准确率. 调查结果有助于决策者了解公众意见,并支持烟草控制工作.
科学领域:
- 公共卫生 公共卫生
- 计算社会科学 计算社会科学
- 数据科学数据科学数据科学
背景情况:
- 在线讨论显著影响公共卫生政策.
- 了解关于减少烟草危害的公共话语对于有效的烟草控制至关重要.
研究的目的:
- 开发和评估一种机器学习方法,用于预测推特中的"减少危害"主题.
- 分析在线烟草控制讨论中的情绪,转发和毒性.
主要方法:
- 潜在的迪里克莱特分配 (LDA) 用于主题建模.
- 随机森林算法用于主题预测,达到91.87%的准确性.
- 推特的情感分析和毒性分析.
主要成果:
- 通过使用LDA.成功归类了"减少危害"的推文.
- 在降低危害主题方面实现了高预测准确度 (91.87%).
- 确定了转发,情绪和对话毒性之间的相关性.
结论:
- 机器学习有效地预测了关于减少烟草危害的在线讨论.
- 对推特情绪和毒性的分析为公众论提供了洞察力.
- 调查结果支持政策制定者对烟草控制政策进行公众参与.
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