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

TL;DR

This paper demonstrates that quasi error-free text classification and authorship recognition are achievable across the entire Gutenberg Literary English Corpus using a consistent set of style and content features, advancing digital humanities research.

Contribution

Introduces a novel, unified feature set and method for accurate text classification and authorship recognition across diverse literary texts in the GLEC.

Findings

High accuracy in text classification and authorship recognition across the corpus

Identification of key diagnostic features including type-token ratio and surprise

A simple, versatile tool applicable to both short poems and long novels

Abstract

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