In the few works that have used NLP to study literary quality, sentiment and emotion analysis
have often been considered valuable sources of information. At the same time, the idea that …

Predicting literary quality and reader appreciation of narrative texts are highly complex
challenges in quantitative and computational literary studies due to the fluid definitions of …

Computational studies of literature use proxies like sales numbers, human judgments, or
canonicity to estimate literary quality. However, many quantitative use one such measure as …

We introduce EmotionArcs, a dataset comprising emotional arcs from over 9,000 English
novels, assembled to understand the dynamics of emotions represented in text and how …

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 …

This study examines gender biases in machine learning models that predict literary
canonicity. Using algorithmic fairness metrics like equality of opportunity, equalised odds …

Computational textual aesthetics is an emerging field that aims to investigate observable
differences between aesthetic categories of text. In this study, we explored structural …