@inproceedings{baric-etal-2023-target,
    title = "Target Two Birds With One {ST}o{N}e: Entity-Level Sentiment and Tone Analysis in {C}roatian News Headlines",
    author = "Bari{\'c}, Ana  and
      Majer, Laura  and
      Duki{\'c}, David  and
      Grbe{\v{s}}a-zenzerovi{\'c}, Marijana  and
      Snajder, Jan",
    editor = "Piskorski, Jakub  and
      Marci{\'n}czuk, Micha{\l}  and
      Nakov, Preslav  and
      Ogrodniczuk, Maciej  and
      Pollak, Senja  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Rybak, Piotr  and
      Steinberger, Josef  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bsnlp-1.10/",
    doi = "10.18653/v1/2023.bsnlp-1.10",
    pages = "78--85",
    abstract = "Sentiment analysis is often used to examine how different actors are portrayed in the media, and analysis of news headlines is of particular interest due to their attention-grabbing role. We address the task of entity-level sentiment analysis from Croatian news headlines. We frame the task as targeted sentiment analysis (TSA), explicitly differentiating between sentiment toward a named entity and the overall tone of the headline. We describe SToNe, a new dataset for this task with sentiment and tone labels. We implement several neural benchmark models, utilizing single- and multi-task training, and show that TSA can benefit from tone information. Finally, we gauge the difficulty of this task by leveraging dataset cartography."
}