Li, Yingjie ; Frans, Veronica F. ; Song, Yongze ; Cai, Meng ; Zhang, Yuqian ; Liu, Jianguo (2023)
SDGdetector: an R-based text mining tool for quantifying efforts toward Sustainable Development Goals.
In: Journal of Open Source Software, 8 (84)
doi: 10.21105/joss.05124
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The global interest in moving towards a sustainable future has grown exponentially at all levels. The United Nations’ Sustainable Development Goals (SDGs), adopted by world leaders in 2015, provide an integrated framework to track progress toward sustainability (UN, 2019). Textual data, such as public statements posted on websites, organization reports, and scientific publications, is a rich source for evaluating the planned and ongoing efforts, as well as achievements towards sustainability. However, no computational tool exists to date that can accurately and efficiently identify SDG-related statements from these large amounts of text data. To fill this gap, we developed the SDGdetector package in R (R Core Team, 2021) to map textual data to specific goals and targets under the UN SDG framework for quantitative analysis. This is the first open-source, high-resolution, and high-accuracy analytical package that can identify which and how many SDG goals and targets are declared in any type of text-based data frame or corpus. This package thus enables a unique way to monitor individuals’ and organizations’ commitments and efforts towards advancing the 17 SDGs and 169 associated targets.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Li, Yingjie ; Frans, Veronica F. ; Song, Yongze ; Cai, Meng ; Zhang, Yuqian ; Liu, Jianguo |
| Art des Eintrags: | Bibliographie |
| Titel: | SDGdetector: an R-based text mining tool for quantifying efforts toward Sustainable Development Goals |
| Sprache: | Englisch |
| Publikationsjahr: | 12 April 2023 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Open Source Software |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| (Heft-)Nummer: | 84 |
| Kollation: | 6 Seiten |
| DOI: | 10.21105/joss.05124 |
| Kurzbeschreibung (Abstract): | The global interest in moving towards a sustainable future has grown exponentially at all levels. The United Nations’ Sustainable Development Goals (SDGs), adopted by world leaders in 2015, provide an integrated framework to track progress toward sustainability (UN, 2019). Textual data, such as public statements posted on websites, organization reports, and scientific publications, is a rich source for evaluating the planned and ongoing efforts, as well as achievements towards sustainability. However, no computational tool exists to date that can accurately and efficiently identify SDG-related statements from these large amounts of text data. To fill this gap, we developed the SDGdetector package in R (R Core Team, 2021) to map textual data to specific goals and targets under the UN SDG framework for quantitative analysis. This is the first open-source, high-resolution, and high-accuracy analytical package that can identify which and how many SDG goals and targets are declared in any type of text-based data frame or corpus. This package thus enables a unique way to monitor individuals’ and organizations’ commitments and efforts towards advancing the 17 SDGs and 169 associated targets. |
| Fachbereich(e)/-gebiet(e): | 13 Fachbereich Bau- und Umweltingenieurwissenschaften 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Verbund Institute für Verkehr 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Verbund Institute für Verkehr > Institut für Verkehrsplanung und Verkehrstechnik |
| Hinterlegungsdatum: | 28 Mär 2024 10:47 |
| Letzte Änderung: | 28 Mär 2024 10:47 |
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