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The Gutenberg Literary English Corpus (GLEC) provides a rich source oftextual data for research in digital humanities, computational linguistics orneurocognitive poetics. However, so far only a small subcorpus, the GutenbergEnglish Poetry Corpus, has been submitted to quantitative text analysesproviding predictions for scientific studies of literature. Here we show thatin the entire GLEC quasi error-free text classification and authorshiprecognition is possible with a method using the same set of five style and fivecontent features, computed via style and sentiment analysis, in both tasks. Ourresults identify two standard and two novel features (i.e., type-token ratio,frequency, sonority score, surprise) as most diagnostic in these tasks. Byproviding a simple tool applicable to both short poems and long novelsgenerating quantitative predictions about features that co-determe thecognitive and affective processing of specific text categories or authors, ourdata pave the way for many future computational and empirical studies ofliterature or experiments in reading psychology.

Quasi Error-free Text Classification and Authorship Recognition in a large Corpus of English Literature based on a Novel Feature Set