Estimating time-series changes in social sentiment @Twitter in U.S. metropolises during the COVID-19 pandemic
This repository contains the input and output data for the research article entitled Estimating time-series changes in social sentiment @Twitter in U.S. metropolises during the COVID-19 pandemic by Ryuichi Saito and Shinichiro Haruyama. The article was published in the Journal of Computational Social Science in 2022.
The collection_data folder contains tweets collected using the Full-archive Search API of Twitter API v2 and the result folder consists of folders tweets sentiment classified by the BERT model and the GPT-3 model and TF-IDF results. The folder of collection data and estimation data consists of 1 to 3 folders, which numbers mean types of state-government orders that restricted citizens' activities.
| Folder No. | Related Tweets Types |
|---|---|
| 1 | Stay-at-home |
| 2 | Restriction-on-gatherings |
| 3 | Travel-restrictions |