Semantic Scaling: Bayesian Ideal Point Estimates with Large Language Models

Abstract: This paper introduces "Semantic Scaling," a novel method for ideal point estimation from text. I leverage LLMs 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.

• The paper introduces Semantic Scaling to estimate ideological positions from text using Bayesian MCMC and advanced language models.
• It offers flexibility by allowing researchers to define ideological dimensions tailored to diverse and variable-length documents.
• Applications include analyzing policy on Twitter and US Congress data, with results that closely match human judgment.

Exploring Semantic Scaling: A Flexible Approach to Estimate Ideological Positions from Text

Introduction to Semantic Scaling

Semantic Scaling is an innovative method introduced by Michael Burnham to estimate ideological points, leveraging the capabilities of LLMs. This approach classifies documents based on semantic meanings to extract survey-like data. This data is then analyzed using Bayesian Markov Chain Monte Carlo techniques within an item response theory framework. By allowing researchers to define the ideological spectrum explicitly, Semantic Scaling not only adapts to different document types and lengths but also promises valid estimates across diverse population segments.

Key Advantages and Methodology

Semantic Scaling distinguishes itself from traditional text-based scaling methods in several profound ways:

• Flexibility in Definition: Unlike previous methods restricted by predefined lexical databases or word vectors, Semantic Scaling permits researchers to tailor the ideological dimensions as per study requirements.
• Broad Applicability: This method is effectively implemented on texts of varying lengths and types, accommodating an extensive range of documents—from tweets to full-length articles.
• Fine-grained Semantic Analysis: By using LLMs, the system understands and classifies nuanced semantic content in documents, enabling more precise extraction of ideological positions compared to methods relying merely on word frequency or vector representations.

The process involves classifying statements from available texts against predefined hypotheses related to ideological stances and then employing Bayesian statistical techniques to estimate the latent ideological traits of document authors.

Applications of Semantic Scaling

1. Analysis of Policy Preferences on Twitter:

Burnham evaluated the applicability of Semantic Scaling by comparing it against Tweetscores, a previously established method, in analyzing Twitter data. The results showed that Semantic Scaling not only recaptures well-accepted results but also tends to align more closely with human judgment in cases of discrepancies between the two methods.

2. Estimating Ideologies in the US Congress:

In another application, Semantic Scaling was applied to derive the ideological positions of members from the 117th US Congress using publicly available documents like tweets and newsletters. It demonstrated strong alignment with DW-NOMINATE scores but offered additional flexibility to analyze dimensions such as in-group/out-group affect which traditional roll-call based methods like DW-NOMINATE could not provide.

Implications and Future Directions

The introduction of Semantic Scaling opens up new avenues for researching and understanding ideological landscapes. The flexibility to define and measure ideological dimensions precisely can immensely benefit studies in political science, sociology, and beyond.

This method has significant potential for exploring affective polarization by separately identifying and analyzing policy preferences and group affect without conflating the two. Moreover, with advancements in AI and the development of more specialized LLMs, the accuracy and efficiency of Semantic Scaling are expected to enhance further.

Conclusion

Semantic Scaling heralds a promising shift in how researchers can derive ideological insights from textual data. By combining the power of LLMs with sophisticated statistical models, this method provides a robust tool for academic and possibly commercial applications, where understanding ideological leanings is crucial. As the landscape of text-based data continues to evolve, so too will the capabilities and applications of Semantic Scaling, making it a valuable component of the analytical toolkit for social scientists and researchers.