Annotating and Training for Population Subjective Views

Maria Alexeeva, Caroline Hyland, Keith Alcock, Allegra A. Beal Cohen, Hubert Kanyamahanga, Isaac Kobby Anni, Mihai Surdeanu


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.
Anthology ID:
2023.wassa-1.36
Volume:
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Jeremy Barnes, Orphée De Clercq, Roman Klinger
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
416–430
Language:
URL:
https://aclanthology.org/2023.wassa-1.36
DOI:
10.18653/v1/2023.wassa-1.36
Bibkey:
Cite (ACL):
Maria Alexeeva, Caroline Hyland, Keith Alcock, Allegra A. Beal Cohen, Hubert Kanyamahanga, Isaac Kobby Anni, and Mihai Surdeanu. 2023. Annotating and Training for Population Subjective Views. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 416–430, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Annotating and Training for Population Subjective Views (Alexeeva et al., WASSA 2023)
Copy Citation:
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