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With the availability of massive amounts of data, online reviews have become a promising source for detecting readers' engagement with fiction. Yet identifying expressions of response to reading (such as emotions, evaluations, and feelings) remains challenging for automated analysis, given the unstructured nature of reviews. This study proposes a method for categorizing review content in order to distinguish between sentences referring to readers' experiences and those addressing other book-related aspects, such as plot, characters, or author. We first designed an annotation schema, after assessing previous classifications. Then, we manually annotated the sentences from 1,400 Dutch fiction reviews according to our schema. Subsequently, we trained a robBERT2023 classifier to automate review sentence classification and applied it to a corpus of 670,751 reviews. Our analysis allows us to draw conclusions on the composition of online reviews and correlations among their components.
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Koolen et al. (2026) studied this question.
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