三种方法的比较,以确定文本数据中的主题
George A Barnett1, Christopher Calabrese2, Jeanette B Ruiz1
1Department of Communication, University of California, Davis, Davis, CA, United States.
Frontiers in research metrics and analytics
|June 19, 2023
概括
社区检测和集群分析比主题建模更清楚地识别大型文本数据集中的主题,而主题建模产生重叠的主题. 这有助于选择最佳的方法来确定主题.
科学领域:
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
- 公共卫生信息学 公共卫生信息学
背景情况:
- 确定大型文本体的主题对于数据解释至关重要.
- 现有的方法如主题建模,社区检测和集群分析都有不同的优缺点.
- 与健康相关的社交媒体数据提供了有价值的见解,但需要强大的分析方法.
研究的目的:
- 为了比较三种不同的方法在大型文本数据集中确定主题的有效性.
- 评估主题建模,社区检测和集群分析,使用现实世界与健康相关的Twitter数据.
- 为选择分析社交媒体内容的适当方法提供指导.
主要方法:
- 使用了三个分析技术:主题建模,社区检测 (语义网络) 和集群分析 (沃德的方法).
- 将这些方法应用于关于艾滋病毒暴露前预防 (PrEP) 和儿童疫苗接种的两个不同的Twitter帖子数据集.
- 通过不同的方法比较了不同方法识别的主题的清晰度和独特性.
主要成果:
- 与主题建模相比,社区检测和集群分析产生了更清晰的定义主题.
- 主题建模产生了更多的主题,但这些经常表现出明显的重叠.
- 方法的选择直接影响文本数据中识别的主题的可解释性和独特性.
结论:
- 语义网络分析 (社区检测) 和集群分析优于主题建模,用于识别与健康相关的Twitter数据中的不同主题.
- 了解方法上的差异是准确解释社交媒体内容的关键.
- 这项比较研究为公共卫生研究选择最佳文本分析技术提供了信息.
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