@inproceedings{maehlum-etal-2023-diagnostic,
    title = "A Diagnostic Dataset for Sentiment and Negation Modeling for {N}orwegian",
    author = "M{\ae}hlum, Petter  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    editor = "Ilinykh, Nikolai  and
      Morger, Felix  and
      Dann{\'e}lls, Dana  and
      Dobnik, Simon  and
      Megyesi, Be{\'a}ta  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)",
    month = may,
    year = "2023",
    address = "T{\'o}rshavn, the Faroe Islands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.resourceful-1.11/",
    pages = "77--85",
    abstract = "Negation constitutes a challenging phenomenon for many natural language processing tasks, such as sentiment analysis (SA). In this paper we investigate the relationship between negation and sentiment in the context of Norwegian professional reviews. The first part of this paper includes a corpus study which investigates how negation is tied to sentiment in this domain, based on existing annotations. In the second part, we introduce NoReC-NegSynt, a synthetically augmented test set for negation and sentiment, to allow for a more detailed analysis of the role of negation in current neural SA models. This diagnostic test set, containing both clausal and non-clausal negation, allows for analyzing and comparing models' abilities to treat several different types of negation. We also present a case-study, applying several neural SA models to the diagnostic data."
}