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Open Access

Opinion Mining Corpus on German Tweets about the Covid-19 Pandemic

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Date

2021-05-31

Type

Dataset
Text
Software

Authors

Beck, Tilman
Lee, Ji-Ung
Viehmann, Christina
Maurer, Marcus
Quiring, Oliver
Gurevych, IrynaORCID 0000-0003-2187-7621

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

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.

Keywords

NLP, Opinion Mining, Social Media, Covid-19

Citation

Identifier

https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2780

Endorsement

Related Resources

Is Cited By
https://arxiv.org/abs/2105.12980

DFG Classification

4.43-06 Datenmanagement, datenintensive Systeme, Informatik-Methoden in der Wirtschaftsinformatik

Project(s)

DFG | GRK2222 | TP Gurevych
EU/EFRE | 20005482 | TexPrax - Gurevych

Faculty

Collections

Argument Mining
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License

CC BY 4.0 - Attribution 4.0 International
Except where otherwise noted, this license is described as CC BY 4.0 - Attribution 4.0 International