dc.contributor.author | Chai, Yuchen | |
dc.contributor.author | Kakkar, Devika | |
dc.contributor.author | Palacios, Juan | |
dc.contributor.author | Zheng, Siqi | |
dc.date.accessioned | 2024-02-15T17:59:40Z | |
dc.date.available | 2024-02-15T17:59:40Z | |
dc.date.issued | 2023-10-09 | |
dc.identifier.issn | 2052-4463 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/153527 | |
dc.description.abstract | Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period. | en_US |
dc.language.iso | en_US | |
dc.publisher | Springer Science and Business Media LLC | en_US |
dc.relation.isversionof | 10.1038/s41597-023-02572-7 | en_US |
dc.rights | Creative Commons Attribution | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Springer Nature | en_US |
dc.subject | Library and Information Sciences | en_US |
dc.subject | Statistics, Probability and Uncertainty | en_US |
dc.subject | Computer Science Applications | en_US |
dc.subject | Education | en_US |
dc.subject | Information Systems | en_US |
dc.subject | Statistics and Probability | en_US |
dc.title | Twitter Sentiment Geographical Index Dataset | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Chai, Y., Kakkar, D., Palacios, J. et al. Twitter Sentiment Geographical Index Dataset. Sci Data 10, 684 (2023). | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.date.submission | 2024-02-15T17:50:40Z | |
mit.journal.volume | 10 | en_US |
mit.journal.issue | 1 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |