| license: unknown | |
| language: | |
| - de | |
| tags: | |
| - historical | |
| - newspapers | |
| # SentiAnno: A Sentiment-Annotated Corpus of Austrian Historical Newspapers | |
| This repository hosts training, development and test splits for the recently introduced "SentiAnno" dataset from the ["Constructing a Sentiment-Annotated Corpus of Austrian Historical | |
| Newspapers: Challenges, Tools, and Annotator Experience"](https://aclanthology.org/2024.nlp4dh-1.6/) paper by [Lucija Krušic](https://huggingface.co/lukru). | |
| More from the paper: | |
| > This study presents the development of a sentiment-annotated corpus of historical newspaper texts in Austrian German, addressing a gap in annotated corpora for Natural Language Processing in the field of Digital Humanities. Three annotators categorised 1005 sentences from two 19th-century periodicals into four sentiment categories: positive, negative, neutral, and mixed. The annotators, Masters and PhD students in Linguistics and Digital Humanities, are considered semi-experts and have received substantial training during this annotation study. | |
| # Dataset Stats | |
| We create a 80/10/10 dataset split from the gold standard annotations, using the [`0ecb222`](https://github.com/lucijakrusic/SentiAnno/tree/0ecb2228e6c290dd22836024f32e559cc9b9711e) revision. | |
| For each label category (`positive`, `negative`, `neutral`, `mixed`) this dataset split ratio is performed leading to: | |
| * 741 training examples | |
| * 93 development examples | |
| * 95 test examples | |
| Dataset splits were created using this [notebook](CreateDatasetSplits.ipynb). | |
| # Dataset Usage | |
| An example [notebook](FlairDatasetLoader.ipynb) shows how to use this dataset with the awesome Flair library. | |
| # Acknowledgements | |
| Many thanks to [Lucija Krušic](https://huggingface.co/lukru) for releasing the SentiAnno dataset! | |
| # License | |
| License is still be to cleared out, for now it is "unknown". |