Opinion Mining Corpus on German Tweets about the Covid-19 Pandemic
| dc.contributor.author | Beck, Tilman | |
| dc.contributor.author | Lee, Ji-Ung | |
| dc.contributor.author | Viehmann, Christina | |
| dc.contributor.author | Maurer, Marcus | |
| dc.contributor.author | Quiring, Oliver | |
| dc.contributor.author | Gurevych, Iryna | |
| dc.date.accessioned | 2021-05-31T11:08:22Z | |
| dc.date.available | 2021-05-31T11:08:22Z | |
| dc.date.issued | 2021-08 | |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2780 | |
| dc.description | The UKP Covid-19 Twitter Corpus includes 2,785 tweets annotated by student annotators and 200 expert-annotated tweets in German. Each tweet was annotated as either a supporting opinion ("Support"), an attacking argument ("Refute"), a commenting statement ("Comment") or unrelated ("Unrelated") with respect to governmental measures taken to prevent the spread of Covid-19. | en_US |
| dc.language.iso | de | en_US |
| dc.relation | IsCitedBy;URL;https://arxiv.org/abs/2105.12980 | |
| dc.rights | Creative Commons Attribution 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | NLP | en_US |
| dc.subject | Opinion Mining | en_US |
| dc.subject | Social Media | en_US |
| dc.subject | Covid-19 | en_US |
| dc.subject.classification | 4.43-06 Datenmanagement, datenintensive Systeme, Informatik-Methoden in der Wirtschaftsinformatik | en_US |
| dc.subject.ddc | 004 | |
| dc.title | Opinion Mining Corpus on German Tweets about the Covid-19 Pandemic | en_US |
| dc.type | Dataset | en_US |
| dc.type | Text | en_US |
| dc.type | Software | en_US |
| tud.project | DFG | GRK2222 | TP Gurevych | en_US |
| tud.project | EU/EFRE | 20005482 | TexPrax - Gurevych | en_US |
| tud.unit | TUDa | |
| tud.history.classification | Version=2020-2024;409-06 Informationssysteme, Prozess- und Wissensmanagement |
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Argument Mining [19]
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