BERTopic_Teen:一个多模块优化方法,用于青少年健康中的短文主题建模
Yiqiang Feng1, Ziao Chen2, Yuxin Zhang3
1School of Marxism, Sichuan Agricultural University, Chengdu, China.
Frontiers in public health
|August 28, 2025
概括
一个名为BERTopic_Teen的新框架, 通过更好地理解青少年健康讨论,
科学领域:
- 数字公共卫生
- 计算社会科学
- 青少年健康监测
背景情况:
- 社交媒体为公众健康提供实时数据.
- 社交媒体上的短文对传统的主题建模提出了挑战.
- 网上的青少年健康讨论需要专门的分析工具.
研究的目的:
- 开发一个针对青少年健康相关推文的优化主题建模框架.
- 解决社交媒体短文中的语义稀疏性和噪音问题.
- 提高从在线青少年健康数据中提取主题的准确性和稳定性
主要方法:
- 拟议的BERTopic_Teen,包括流行偏差调节器 (PDR),动态文档嵌入优化器 (DDEO) 和概率重新分配矩阵 (PRM).
- 我们使用了64,441条青少年健康推文的数据集.
- 将BERTopic_Teen与LDA,NMF,Top2Vec和原来的BERTopic进行比较.
主要成果:
- 与现有方法相比,BERTopic_Teen在所有评估指标上都表现出更好的表现.
- 在主题一致性方面取得了16.1%的改善 (NPMI = 0.2184).
- 报告了较高的主题多样性 (TD = 0.9935) 和较低的困惑性 (1.7214).
结论:
- 在社交媒体上分析青少年健康问题.
- 该框架增强了语义清晰度,主题独特性和建模稳定性.
- 这些发现支持通过改善社交媒体数据分析来推进公共卫生监测.
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