@inproceedings{moreira-etal-2023-modeling,
    title = "Modeling Readers{'} Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles",
    author = "Moreira, Pascale  and
      Bizzoni, Yuri  and
      Nielbo, Kristoffer  and
      Lassen, Ida Marie  and
      Thomsen, Mads",
    editor = "Akoury, Nader  and
      Clark, Elizabeth  and
      Iyyer, Mohit  and
      Chaturvedi, Snigdha  and
      Brahman, Faeze  and
      Chandu, Khyathi",
    booktitle = "Proceedings of the 5th Workshop on Narrative Understanding",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.wnu-1.5",
    doi = "10.18653/v1/2023.wnu-1.5",
    pages = "25--35",
    abstract = "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.",
}

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.
Moreira, Pascale Bizzoni, Yuri Nielbo, Kristoffer Lassen, Ida Marie Thomsen, Mads
Modeling Readers' Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles