绘制"X"辩论:用先进的机器学习进行水化情绪分析.
Nilesh Torwane1, Ratilal Lalloo1, Diep Ha1
1School of Dentistry, University of Queensland, Herston, Australia.
Journal of public health dentistry
|May 7, 2025
概括
关于社区水化 (CWF) 的公众情绪是两极分化的,参与度随着时间的推移而波动. 了解这些趋势有助于为CWF制定有针对性的公共卫生传播策略.
科学领域:
- 公共卫生 公共卫生
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 社区水化 (CWF) 是预防牙损伤的关键公共卫生措施.
- 公众论对公共卫生倡议的采用和可持续性产生重大影响.
- 社交媒体平台提供了一个庞大的数据集,用于实时了解公众情绪.
研究的目的:
- 在过去的十年中,利用社交媒体数据分析公众对社区水化 (CWF) 的情绪.
- 确定与CWF相关的论趋势和关键讨论主题.
- 为CWF提供有针对性的公共卫生传播策略.
主要方法:
- 使用情感强度分析器对与CWF相关的推文进行情感分析.
- 单词共发生网络分析,以确定突出的讨论主题.
- 机器学习模型 (例如,逻辑回归) 的比较,以确定情绪分类的准确性.
主要成果:
- 对CWF的公众情绪几乎是平衡的:37.4%是负面的,34.4%是积极的,28.2%是中立的.
- 参与度在2015-2016年达到顶峰,在2018年后下降,与政策和媒体相关的尖端.
- 关键主题包括安全和牙科健康;物流回归在情绪分类中显示出最高的准确性.
结论:
- 关于CWF的公众情绪是两极分化和动态的,需要适应性的沟通策略.
- 针对中立受众的有针对性的信息传递和打击错误信息对于建立信任至关重要.
- 机器学习增强了实时监测,并支持基于证据的公共卫生决策.
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