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rapidcodeR: Fast, Easy, and Affordable Data Coding with LLMs

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  • Lönn, Gabriel Elias

    (University of Oslo)

  • Schutte, Sebastian

Abstract

Text-as-data methods aim to extract quantitative information from natural language. Traditionally, this was accomplished by using separate steps in each analysis, such as part-of-speech tagging, sentiment analysis, and named entity recognition. Modern Large Language Models (LLMs) hold the promise of drastically simplifying this process, as they can be flexibly instructed to extract specific information. However, transferring data to LLM providers at scale and receiving directly usable data back has previously required application-specific programming. In contrast, the rapidcodeR package offers a highly flexible approach to coding quantitative data from text at maximum speed and minimal cost. Here, we present an example of using the package to visualize international relations, directly coded from Russian UN speeches between 1946 and 2024. The results correspond well to formal alliance structures. Similar use cases can involve coding of archival information, researching social media discourse, and extracting event data from news sources.

Suggested Citation

  • Lönn, Gabriel Elias & Schutte, Sebastian, 2026. "rapidcodeR: Fast, Easy, and Affordable Data Coding with LLMs," SocArXiv q4zd8_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:q4zd8_v1
    DOI: 10.31219/osf.io/q4zd8_v1
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