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

Gabriel Elias Lönn and Sebastian Schutte
Additional contact information
Gabriel Elias Lönn: University of Oslo

No q4zd8_v1, SocArXiv from Center for Open Science

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.

Date: 2026-07-22
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/6a60d9c1e310397613723287/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:q4zd8_v1

DOI: 10.31219/osf.io/q4zd8_v1

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().