Word embeddings represent a powerful tool for mining the vocabularies of literary and historical text. However, there is little research demonstrating appropriate strategies for representing text and setting parameters, when constructing embedding models within a digital humanities context. In this paper we examine the effects of these choices using a case study involving 18th and 19th century texts from the British Library. The study demonstrates the importance of examining implicit assumptions around default strategies, when using embeddings with literary texts and highlights the potential of quantitative analysis to inform critical analysis
