Shifting Sentiments? What happens to BERT-based Sentiment Classification when derived text formats are used for fine-tuning

Existing copyright law in the European Union, the United States of America and many other jurisdictions around the world means that Text and Data Mining (TDM) encounters several limitations concerning the storage, publication, and re-use of datasets derived from copyrighted texts. As a response, derived text formats (DTFs), also known as extracted features for non-consumptive research, have been proposed and used (see e.g. Rehm et al. 2007, Lin et al. 2012, Jett et al. 2020, Schöch et al. 2020). The core concept is to selectively remove specific pieces of information, particularly copyright-relevant features, from in-copyright texts. Put simply, such textscannot be read by people, so that making them available to the public does not violate copyright law. At the same time, the texts are still suitable for a variety of TDM tasks in digital humanities (DH), such as authorship attribution or topic modeling. There appears to be a trade-off between usefulness for research and safe use with respect to copyright. To better understand this and define DTFs that lie at a sweet spot combining usefulness for research and safety from a copyright perspective, a series of evaluations is needed. More precisely, the impact of information loss caused by DTFs on the performance of different TDM tasks must be assessed. In this work, we focus on evaluating two token-based DTFs on a common task in DH, namely BERT-based sentiment classification.