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The Gutenberg Literary English Corpus (GLEC, Jacobs, 2018a) provides a richsource of textual data for research in digital humanities, computationallinguistics or neurocognitive poetics. In this study we address differencesamong the different literature categories in GLEC, as well as differencesbetween authors. We report the results of three studies providing i) topic andsentiment analyses for six text categories of GLEC (i.e., children and youth,essays, novels, plays, poems, stories) and its >100 authors, ii) novel measuresof semantic complexity as indices of the literariness, creativity and bookbeauty of the works in GLEC (e.g., Jane Austen's six novels), and iii) twoexperiments on text classification and authorship recognition using novelfeatures of semantic complexity. The data on two novel measures estimating atext's literariness, intratextual variance and stepwise distance (vanCranenburgh et al., 2019) revealed that plays are the most literary texts inGLEC, followed by poems and novels. Computation of a novel index of textcreativity (Gray et al., 2016) revealed poems and plays as the most creativecategories with the most creative authors all being poets (Milton, Pope, Keats,Byron, or Wordsworth). We also computed a novel index of perceived beauty ofverbal art (Kintsch, 2012) for the works in GLEC and predict that Emma is thetheoretically most beautiful of Austen's novels. Finally, we demonstrate thatthese novel measures of semantic complexity are important features for textclassification and authorship recognition with overall predictive accuracies inthe range of .75 to .97. Our data pave the way for future computational andempirical studies of literature or experiments in reading psychology and offermultiple baselines and benchmarks for analysing and validating other bookcorpora.

Computational analyses of the topics, sentiments, literariness, creativity and beauty of texts in a large Corpus of English Literature