Twitter Sentiment Geographical Index Dataset
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Springer Science and Business Media LLC
2024
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| Online Access: | https://hdl.handle.net/1721.1/153527 |
| _version_ | 1826214027266621440 |
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| author | Chai, Yuchen Kakkar, Devika Palacios, Juan Zheng, Siqi |
| author_facet | Chai, Yuchen Kakkar, Devika Palacios, Juan Zheng, Siqi |
| author_sort | Chai, Yuchen |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:58:39Z |
| format | Article |
| id | mit-1721.1/153527 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:58:39Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1535272024-02-16T03:05:54Z Twitter Sentiment Geographical Index Dataset Chai, Yuchen Kakkar, Devika Palacios, Juan Zheng, Siqi Library and Information Sciences Statistics, Probability and Uncertainty Computer Science Applications Education Information Systems Statistics and Probability 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. 2024-02-15T17:59:40Z 2024-02-15T17:59:40Z 2023-10-09 Article http://purl.org/eprint/type/JournalArticle 2052-4463 https://hdl.handle.net/1721.1/153527 Chai, Y., Kakkar, D., Palacios, J. et al. Twitter Sentiment Geographical Index Dataset. Sci Data 10, 684 (2023). en_US 10.1038/s41597-023-02572-7 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Springer Nature |
| spellingShingle | Library and Information Sciences Statistics, Probability and Uncertainty Computer Science Applications Education Information Systems Statistics and Probability Chai, Yuchen Kakkar, Devika Palacios, Juan Zheng, Siqi Twitter Sentiment Geographical Index Dataset |
| title | Twitter Sentiment Geographical Index Dataset |
| title_full | Twitter Sentiment Geographical Index Dataset |
| title_fullStr | Twitter Sentiment Geographical Index Dataset |
| title_full_unstemmed | Twitter Sentiment Geographical Index Dataset |
| title_short | Twitter Sentiment Geographical Index Dataset |
| title_sort | twitter sentiment geographical index dataset |
| topic | Library and Information Sciences Statistics, Probability and Uncertainty Computer Science Applications Education Information Systems Statistics and Probability |
| url | https://hdl.handle.net/1721.1/153527 |
| work_keys_str_mv | AT chaiyuchen twittersentimentgeographicalindexdataset AT kakkardevika twittersentimentgeographicalindexdataset AT palaciosjuan twittersentimentgeographicalindexdataset AT zhengsiqi twittersentimentgeographicalindexdataset |