SentiANNO: Annotating Sentiment in Austrian Historical Newspapers
Year: 2025
Type: article
Abstract: This contribution presents SentiANNO, a sentiment-annotated corpus from historical Austrian newspapers; and describes the annotation process and the training of annotators. The corpus, which covers journalistic texts in German from 1800 to 1938, addresses the lack of sentiment-annotated resources for historical newspapers. Such a resource can be used to fine-tune existing Machine Learning models for Sentiment Analysis.The corpus includes texts from ANNO and DIGITARIUM collections, categorized into four sentiment categories (positive, negative, neutral and mixed) by three non-expert annotators. Throughout the annotation process, Doccano proved to be the most effective annotation tool, with preliminary results showing over 70% inter-annotator agreement despite genre and language complexity. The corpus will be publicly available on Zenodo, supporting open access.
Authors: Krusic, Lucija, Hochreiter, Clara, Frauendorfer, Melanie
Institution: University of Graz
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930620445
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