用大型语言模型进行定性研究:深度计算文本分析器 (DECOTA)
Lois Player1, Ryan Hughes2, Kaloyan Mitev1
1Department of Psychology, University of Bath.
Psychological methods
|April 7, 2025
概括
深度计算文本分析器 (DECOTA) 提供了一种新的自动机器学习方法,用于分析大型文本数据集. 与人类编码相比,该工具大大减少了时间和成本,为研究和政策提供了可靠的主题分析.
科学领域:
- 计算语言学计算语言学
- 机器学习 机器学习
- 定性数据分析是指对数据进行定性分析.
背景情况:
- 在研究和政策中,越来越需要快速分析大型定性数据集.
- 目前的机器辅助方法需要专家的人类解释,从而造成可访问性障碍.
- 需要为主题分析提供自动化,成本效益高的解决方案.
研究的目的:
- 提出和验证深度计算文本分析器 (DECOTA),这是一种用于自动化自由文本分析的新型机器学习方法.
- 开发和测试一种自动化算法,用于在结构性主题建模中选择最佳的主题数量.
- 与人类编码相比,评估DECOTA的速度,成本效益和准确性.
主要方法:
- 利用结构主题建模,使用两个微调的大型语言模型和句子转换器.
- 开发了一种新的算法,可以自动确定最佳的主题数量.
- 通过将其输出 (代码,主题,流行率) 与四个数据集上的人为编码的主题分析进行比较,验证了DECOTA.
主要成果:
- DECOTA的速度大约是人类编码的378倍,价格是人类编码的1920倍.
- 与人类编码达成高度一致,平均91.6%的代码和90%的主题.
- DECOTA成功地识别了关键主题,它们的流行率,以及人口统计学共变量的变化.
结论:
- DECOTA提供了一种经过验证,自动化和高效的方法来对大型自由文本数据集进行主题分析.
- 提供了克服与人类解释相关的财务和实际障碍的巨大潜力.
- 为基于证据的政策制定,公众参与和心理测量措施的创建提供及时的见解.
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