@inproceedings{alexeeva-etal-2023-annotating,
    title = "Annotating and Training for Population Subjective Views",
    author = "Alexeeva, Maria  and
      Hyland, Caroline  and
      Alcock, Keith  and
      Cohen, Allegra A. Beal  and
      Kanyamahanga, Hubert  and
      Anni, Isaac Kobby  and
      Surdeanu, Mihai",
    editor = "Barnes, Jeremy  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman",
    booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.wassa-1.36",
    doi = "10.18653/v1/2023.wassa-1.36",
    pages = "416--430",
    abstract = "In this paper, we present a dataset of subjective views (beliefs and attitudes) held by individuals or groups. We analyze the usefulness of the dataset by training a neural classifier that identifies belief-containing sentences that are relevant for our broader project of interest{---}scientific modeling of complex systems. We also explore and discuss difficulties related to annotation of subjective views and propose ways of addressing them.",
}

In this paper, we present a dataset of subjective views (beliefs and attitudes) held by individuals or groups. We analyze the usefulness of the dataset by training a neural classifier that identifies belief-containing sentences that are relevant for our broader project of interest{---}scientific modeling of complex systems. We also explore and discuss difficulties related to annotation of subjective views and propose ways of addressing them.
Alexeeva, Maria Hyl , Caroline Alcock, Keith Cohen, Allegra A. Beal Kanyamahanga, Hubert Anni, Isaac Kobby Surdeanu, Mihai
Annotating and Training for Population Subjective Views