Abstract
This paper introduces "Semantic Scaling," a novel method for ideal point estimation from text. I leverage large language models to classify documents based on their expressed stances and extract survey-like data. I then use item response theory to scale subjects from these data. Semantic Scaling significantly improves on existing text-based scaling methods, and allows researchers to explicitly define the ideological dimensions they measure. This represents the first scaling approach that allows such flexibility outside of survey instruments and opens new avenues of inquiry for populations difficult to survey. Additionally, it works with documents of varying length, and produces valid estimates of both mass and elite ideology. I demonstrate that the method can differentiate between policy preferences and in-group/out-group affect. Among the public, Semantic Scaling out-preforms Tweetscores according to human judgement; in Congress, it recaptures the first dimension DW-NOMINATE while allowing for greater flexibility in resolving construct validity challenges.
Abstract (translated)
本文介绍了一种名为"语义扩展"的新方法，用于从文本中进行理想点估计。我利用大型语言模型对文献进行分类，并提取类似于调查的数据。然后使用项目反应理论对被试进行扩展。语义扩展在现有基于文本的扩展方法方面显著改进，并允许研究人员明确定义他们衡量的意识形态维度。这代表了一种允许在调查工具之外具有这种灵活性的扩展方法，为难以进行调查的人口打开了新的研究途径。此外，它与具有不同长度的文档一起工作，并能够同时生成质量和精英意识形态的有效估计。我证明了该方法可以区分政策偏好和群体内/群体外影响。在人群中，语义扩展根据人类判断胜过推特分数；在国会中，它重新捕获了第一维度DW-NOMINATE，同时允许在解决构建真实性挑战方面具有更大的灵活性。
URL
https://arxiv.org/abs/2405.02472
