This repository contains replication data for the article: Griswold, Robbins, and Pollard. (2025). "Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Models". Political Analysis.


Project data included tweets collected from Twitter between Jan 1st. 2020 and Jan 31st, 2021, for a random subset of general Twitter users and for all members of congress. Tweets were coded for stance concerning candidates in the 2020 US Presidential election. For more details, please see the referenced paper.

This repository also includes stance estimates modeled using several OpenAI Large Language Models. These estimates are included for researchers who are unable to reproduce these results using scripts from the code repository, since these models require proprietary access.

Code related to this project can be found at the following repository: https://github.com/maxgriswold/Stay-Tuned---Improving-Sentiment-Analysis-and-Stance-Detection-Using-Large-Language-Models

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Replication Data for: "Stay Tuned - Improving Sentiment Analysis and Stance Detection Using Large Language Models"
Jun 18, 2025
Griswold, Max; Robbins, Michael; Pollard, Michael, 2025, "Replication Data for: "Stay Tuned - Improving Sentiment Analysis and Stance Detection Using Large Language Models"", https://doi.org/10.7910/DVN/OOSYCN, Harvard Dataverse, V2, UNF:6:4M0kCm0nwcDc0ly4/7IBCg== [fileUNF]
Contains input files and supplement files to replicate analysis. Please see related GitHub repository at https://github.com/maxgriswold/Stay-Tuned---Improving-Sentiment-Analysis-and-Stance-Detection-Using-Large-Language-Models. This repository includes files from additional studies, found in the "supplement" folder. Please cite these studies when u...
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