Kristoffer Nielbo


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Good Reads and Easy Novels: Readability and Literary Quality in a Corpus of US-published Fiction
Yuri Bizzoni | Pascale Moreira | Nicole Dwenger | Ida Lassen | Mads Thomsen | Kristoffer Nielbo
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

In this paper, we explore the extent to which readability contributes to the perception of literary quality as defined by two categories of variables: expert-based (e.g., Pulitzer Prize, National Book Award) and crowd-based (e.g., GoodReads, WorldCat). Based on a large corpus of modern and contemporary fiction in English, we examine the correlation of a text’s readability with its perceived literary quality, also assessing readability measures against simpler stylometric features. Our results show that readability generally correlates with popularity as measured through open platforms such as GoodReads and WorldCat but has an inverse relation with three prestigious literary awards. This points to a distinction between crowd- and expert-based judgments of literary style, as well as to a discrimination between fame and appreciation in the reception of a book.

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Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features
Yuri Bizzoni | Pascale Moreira | Mads Rosendahl Thomsen | Kristoffer Nielbo
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

Over the years, the task of predicting reader appreciation or literary quality has been the object of several studies, but it remains a challenging problem in quantitative literary studies and computational linguistics alike, as its definition can vary a lot depending on the genre, the adopted features and the annotation system. This paper attempts to evaluate the impact of sentiment arc modelling versus more classical stylometric features for user-ratings of novels. We run our experiments on a corpus of English language narrative literary fiction from the 19th and 20th century, showing that syntactic and surface-level features can be powerful for the study of literary quality, but can be outperformed by sentiment-characteristics of a text.

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Readability and Complexity: Diachronic Evolution of Literary Language Across 9000 Novels
Pascale Feldkamp | Yuri Bizzoni | Ida Marie S. Lassen | Mads Rosendahl Thomsen | Kristoffer Nielbo
Proceedings of the Joint 3rd International Conference on Natural Language Processing for Digital Humanities and 8th International Workshop on Computational Linguistics for Uralic Languages

Using a large corpus of English language novels from 1880 to 2000, we compare several textual features associated with literary quality, seeking to examine developments in literary language and narrative complexity through time. We show that while we find a correlation between the features, readability metrics are the only ones that exhibit a steady evolution, indicating that novels become easier to read through the 20th century but not simpler. We discuss the possibility of cultural selection as a factor and compare our findings with a subset of canonical works.

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Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles
Pascale Moreira | Yuri Bizzoni | Kristoffer Nielbo | Ida Marie Lassen | Mads Thomsen
Proceedings of the The 5th Workshop on Narrative Understanding

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 quality and the vast feature space that can be considered when modeling a literary work. This paper investigates the potential of sentiment arcs combined with topical-semantic profiling of literary narratives as indicators for their literary quality. Our experiments focus on a large corpus of 19th and 20the century English language literary fiction, using GoodReads’ ratings as an imperfect approximation of the diverse range of reader evaluations and preferences. By leveraging a stacked ensemble of regression models, we achieve a promising performance in predicting average readers’ scores, indicating the potential of our approach in modeling literary quality.


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Predicting Literary Quality How Perspectivist Should We Be?
Yuri Bizzoni | Ida Marie Lassen | Telma Peura | Mads Rosendahl Thomsen | Kristoffer Nielbo
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

Approaches in literary quality tend to belong to two main grounds: one sees quality as completely subjective, relying on the idiosyncratic nature of individual perspectives on the apperception of beauty; the other is ground-truth inspired, and attempts to find one or two values that predict something like an objective quality: the number of copies sold, for example, or the winning of a prestigious prize. While the first school usually does not try to predict quality at all, the second relies on a single majority vote in one form or another. In this article we discuss the advantages and limitations of these schools of thought and describe a different approach to reader’s quality judgments, which moves away from raw majority vote, but does try to create intermediate classes or groups of annotators. Drawing on previous works we describe the benefits and drawbacks of building similar annotation classes. Finally we share early results from a large corpus of literary reviews for an insight into which classes of readers might make most sense when dealing with the appreciation of literary quality.


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Sentiment Dynamics of Success: Fractal Scaling of Story Arcs Predicts Reader Preferences
Yuri Bizzoni | Telma Peura | Mads Rosendahl Thomsen | Kristoffer Nielbo
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

e explore the correlation between the sentiment arcs of H. C. Andersen’s fairy tales and their popularity, measured as their average score on the platform GoodReads. Specifically, we do not conceive a story’s overall sentimental trend as predictive per se, but we focus on its coherence and predictability over time as represented by the arc’s Hurst exponent. We find that degrading Hurst values tend to imply degrading quality scores, while a Hurst exponent between .55 and .65 might indicate a “sweet spot” for literary appreciation.