The Gutenberg Literary English Corpus (GLEC, Jacobs, 2018a) provides a rich
source of textual data for research in digital humanities, computational
linguistics or neurocognitive poetics. In this study we address differences
among the different literature categories in GLEC, as well as differences
between authors. We report the results of three studies providing i) topic and
sentiment analyses for six text categories of GLEC (i.e., children and youth,
essays, novels, plays, poems, stories) and its >100 authors, ii) novel measures
of semantic complexity as indices of the literariness, creativity and book
beauty of the works in GLEC (e.g., Jane Austen's six novels), and iii) two
experiments on text classification and authorship recognition using novel
features of semantic complexity. The data on two novel measures estimating a
text's literariness, intratextual variance and stepwise distance (van
Cranenburgh et al., 2019) revealed that plays are the most literary texts in
GLEC, followed by poems and novels. Computation of a novel index of text
creativity (Gray et al., 2016) revealed poems and plays as the most creative
categories with the most creative authors all being poets (Milton, Pope, Keats,
Byron, or Wordsworth). We also computed a novel index of perceived beauty of
verbal art (Kintsch, 2012) for the works in GLEC and predict that Emma is the
theoretically most beautiful of Austen's novels. Finally, we demonstrate that
these novel measures of semantic complexity are important features for text
classification and authorship recognition with overall predictive accuracies in
the range of .75 to .97. Our data pave the way for future computational and
empirical studies of literature or experiments in reading psychology and offer
multiple baselines and benchmarks for analysing and validating other book
corpora.

该研究报告了对 GLEC 中多个文学类别和作者的主题、情感和语义复杂度进行分析的结果，并提出了衡量文学性、创意和文本美感的新指标。同时，他们还提出了这些指标可以作为文本分类和作者识别的重要特征，并通过实验证明了其准确率。