Semantic Scaling: Bayesian Ideal Point Estimates with Large Language Models
<|reference_start|>Semantic Scaling: Bayesian Ideal Point Estimates with Large Language Models: 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.<|reference_end|>
@article{burnham2024semantic,
    title={Semantic Scaling: Bayesian Ideal Point Estimates with Large Language
  Models},
    author={Michael Burnham},
    journal={arXiv preprint arXiv:2405.02472},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02472},
    primaryClass={cs.CL}
}