Dimensions of Quality: Contrasting Stylistic vs. Semantic Features for Modelling Literary Quality in 9,000 Novels

Abstract

In computational literary studies, the challenging task of predicting quality or reader appreciation of narrative texts is confounded by volatile definitions of quality and the vast feature space that may be considered in modeling. In this paper, we explore two different types of feature sets: stylistic features on one hand, and semantic and sentiment features on the other. We conduct experiments on a corpus of 9,089 English language literary novels published in the 19th and 20th century, using GoodReads' ratings as a proxy for reader appreciation. Examining the potential of both approaches, we find that some types of books are more predictable in one model than in the other, which may indicate that texts have different prominent characteristics (i.a., stylistic complexity, narrative progression at the sentiment-level).