@inproceedings{bizzoni-feldkamp-2023-comparing,
    title = "Comparing Transformer and Dictionary-based Sentiment Models for Literary Texts: Hemingway as a Case-study",
    author = "Bizzoni, Yuri  and
      Feldkamp, Pascale",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Pirinen, Flammie  and
      Alnajjar, Khalid  and
      Miyagawa, So  and
      Bizzoni, Yuri  and
      Partanen, Niko  and
      Rueter, Jack},
    booktitle = "Proceedings of the Joint 3rd International Conference on Natural Language Processing for Digital Humanities and 8th International Workshop on Computational Linguistics for Uralic Languages",
    month = dec,
    year = "2023",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.nlp4dh-1.25/",
    pages = "219--228",
    abstract = "The literary domain continues to pose a challenge for Sentiment Analysis methods, due to its particularly nuanced and layered nature. This paper explores the adequacy of different Sentiment Analysis tools - from dictionary-based approaches to state-of-the-art Transformers - for capturing valence and modelling sentiment arcs. We take Ernest Hemingway{'}s novel The Old Man and the Sea as a case study to address challenges inherent to literary language, compare Transformer and rule-based systems' scores with human annotations, and shed light on the complexities of analyzing sentiment in narrative texts. Finally, we emphasize the potential of model ensembles."
}