Sentimental Matters: Predicting Literary Quality by Sentiment Analysis and Stylometric Features

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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.
TitelProceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
ForlagAssociation for Computational Linguistics
Publikationsdatojul. 2023
ISBN (Trykt)978-1-959429-87-6
StatusUdgivet - jul. 2023


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