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This study contributes to the ongoing discussion on how to operationalize text similarity for the purposes of computational literary studies by defining, justifying theoretically and employing a multi-dimensional text model. Additionally, we evaluate a set of strategies to implement this model for very short texts like poetry using a range of methods from weighted sparse vectors up to very recent neural sentence embeddings based on annotations of emotions, genre and similarity. And finally, we show the relevance of using such a complex text model by applying the best method to a research question about the development of early modernism in German poetry. While we can confirm some important hypotheses from literary studies, we are also able to differentiate or relativize others. In particular, our findings do not support the widely held thesis that the change from realism to modernism was a revolutionary ‘rupture’.

Urspr. Konferenzveröffentlichung/Originally conference publication: 1st Annual Conference of Computational Literary Studies, 01.-02.06.2022, Darmstadt, Germany