在社会科学中使用机器学习的简短文本分类:Twitter上的气候变化案例
Karina Shyrokykh1, Max Girnyk1, Lisa Dellmuth1
1Department of Economic History and International Relations, Stockholm University, Stockholm, Sweden.
PloS one
|September 29, 2023
概括
监督机器学习方法超过了社会科学中文本分类的词典. 像逻辑回归这样的传统方法与深度学习一样有效,但需要更少的资源.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 计算社会科学 计算社会科学
背景情况:
- 社会科学研究越来越需要文字分析.
- 对于大型数据集来说,手动标记文本通常是不可行的.
- 机器学习 (ML) 提供了自动化的文本分类,但在社会科学中没有得到充分的研究.
研究的目的:
- 在典型的社会科学研究场景中比较广泛使用的文本分类器的性能.
- 评估在小型标记数据集上使用ML方法,在大型无标记数据集中使用不常见的类别.
- 分析Twitter关于气候变化的沟通作为一个案例研究.
主要方法:
- 应用和比较监督机器学习分类器.
- 利用了来自国际组织的5750条关于气候变化的推文的新数据集.
- 基于气候变化相关性的评估分类绩效.
主要成果:
- 与最先进的词典相比,监督的ML方法显示出更高的性能,特别是在增加类平衡的情况下.
- 传统的ML方法 (逻辑回归,随机森林) 的性能与深度学习方法相美.
- 传统的ML方法需要的培训时间和计算资源要少得多.
结论:
- 监督的ML对于社会科学研究中的自动化文本分类是有效的.
- 传统的ML方法为文本分析提供了对深度学习的资源高效替代方案.
- 这些发现对分析短文本,如社交媒体数据,在社会科学中具有重大意义.
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