https://github.com/cssmodels/howtousellms

This offers a Jupyter Notebook introduction on how to use Large Language Models for text analysis within the social sciences.
https://github.com/cssmodels/howtousellms

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This offers a Jupyter Notebook introduction on how to use Large Language Models for text analysis within the social sciences.

• Host: GitHub
• URL: https://github.com/cssmodels/howtousellms
• Owner: cssmodels
• Created: 2023-07-24T09:09:07.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-19T16:10:40.000Z (about 1 year ago)
• Last Synced: 2023-09-19T18:56:43.671Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 8.49 MB
• Stars: 13
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# How to use LLMs for Text Analysis

This guide introduces Large Language Models (LLM) as a highly versatile text analysis method within the social sciences. As LLMs are easy-to-use, cheap, fast, and applicable on a broad range of text analysis tasks, ranging from text annotation and classification to sentiment analysis and critical discourse analysis, many scholars believe that LLMs will transform how we do text analysis. This how-to guide is aimed at students and researchers with limited programming experience, and offers a simple introduction to how LLMs can be used for text analysis in your own research project, as well as advice on best practices. We will go through each of the steps of analyzing textual data with LLMs using Python: installing the software, setting up the API, loading the data, developing an analysis prompt, analyzing the text, and validating the results. As an illustrative example, we will use the challenging task of identifying populism in political texts, and show how LLMs move beyond the existing state-of-the-art.