  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.
