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Summary

Large language models (LLMs) can efficiently create reliable text analysis taxonomies. This method reduces analysis time by 87.5% and achieves high human-LLM agreement, proving LLMs

Keywords:
Artificial intelligenceGPTLarge language modelsQualitative analysisTaxonomiesText analysisTutorial

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Area of Science:

  • Computational Social Science
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Manual text analysis is time-consuming, labor-intensive, and prone to bias.
  • Large Language Models (LLMs) offer a promising alternative for efficient and high-quality text analysis.
  • Developing data-driven (bottom-up) taxonomies with LLMs requires structured methodologies.

Purpose of the Study:

  • To present a step-by-step tutorial for developing, testing, and applying taxonomies using LLMs.
  • To demonstrate an iterative and collaborative process between researchers and LLMs for taxonomy creation.
  • To showcase the potential of LLMs in generating reliable bottom-up categorizations for unstructured data.

Main Methods:

  • Utilized personal goals as example data for taxonomy development.
  • Employed prompt engineering to guide LLM in reviewing datasets and generating an initial taxonomy.
  • Iteratively refined the taxonomy through prompt modifications and direct researcher input.
  • Applied the finalized taxonomy to categorize a complete dataset, assessing intercoder reliability.

Main Results:

  • Achieved high human-LLM intercoder agreement in categorizing the dataset.
  • Reduced text analysis time by approximately 87.5% compared to traditional methods.
  • Demonstrated high intercoder reliability using the LLM-generated taxonomy.

Conclusions:

  • LLMs can be effectively utilized to generate reliable bottom-up categorizations for unstructured text data.
  • The presented iterative, collaborative approach facilitates efficient taxonomy development and application.
  • This methodology offers a scalable solution for complex text analysis tasks, reducing bias and improving efficiency.