README.md

---
language:
- de
multilinguality:
  - monolingual
license: cc-by-4.0
pretty_name: SB10k
task_categories:
- text-classification
tags: 
- Sentiment Analysis
size_categories:
- 1K<n<10K
configs:
- config_name: default
  sep: "\t"
  column_names: ["ID", "Sentiment", "Text", "Normalized", "POS-Tags", "Dependency Labels", "additional Annotations"]
  data_files:
  - split: train
    path: "train.tsv"
  - split: test
    path: "test.tsv"
  - split: dev
    path: "dev.tsv"
---
# A Twitter corpus and benchmark resources for german sentiment analysis

### Source
The data is a snapshot from the [SB10k Dataset](https://spinningbytes.com/more/resources/).
The snapshot was made by [Oliver Guhr](https://github.com/oliverguhr/german-sentiment).

### Citation Information
[Paper](https://doi.org/10.21256/zhaw-1530)
```
@inproceedings{cieliebak2017twitter,
  title={A twitter corpus and benchmark resources for german sentiment analysis},
  author={Cieliebak, Mark and Deriu, Jan Milan and Egger, Dominic and Uzdilli, Fatih},
  booktitle={5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017},
  pages={45--51},
  year={2017},
  organization={Association for Computational Linguistics}
}
```