---
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".