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Disentangled learning of stance and aspect topics for vaccine attitude detection in social media
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Zhu, Lixing, Fang, Zheng, Pergola, Gabriele, Procter, Rob and He, Yulan (2022) Disentangled learning of stance and aspect topics for vaccine attitude detection in social media. In: 2022 Conference of the North American Chapter of the Association for Computational Linguistics, Seattle, United States, 10–15 Jul 2022. Published in: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics pp. 1566-1580. doi:10.18653/v1/2022.naacl-main.112
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Official URL: https://doi.org/10.18653/v1/2022.naacl-main.112
Abstract
Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data. Existing approaches have relied heavily on supervised training that requires abundant annotations and pre-defined aspect categories. Instead, with the aim of leveraging the large amount of unannotated data now available on vaccination, we propose a novel semi-supervised approach for vaccine attitude detection, called VADet. A variational autoencoding architecture based on language models is employed to learn from unlabelled data the topical information of the domain. Then, the model is fine-tuned with a few manually annotated examples of user attitudes. We validate the effectiveness of VADet on our annotated data and also on an existing vaccination corpus annotated with opinions on vaccines. Our results show that VADet is able to learn disentangled stance and aspect topics, and outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering.
Item Type: | Conference Item (Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Journal or Publication Title: | Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics | ||||
Publisher: | Association for Computational Linguistics | ||||
Place of Publication: | Seattle, United States | ||||
Official Date: | 10 July 2022 | ||||
Dates: |
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Page Range: | pp. 1566-1580 | ||||
DOI: | 10.18653/v1/2022.naacl-main.112 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Copyright Holders: | Association for Computational Linguistics | ||||
Date of first compliant deposit: | 25 October 2022 | ||||
Date of first compliant Open Access: | 25 October 2022 | ||||
Funder: | EPSRC, University of Warwick, UKRI | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 2022 Conference of the North American Chapter of the Association for Computational Linguistics | ||||
Type of Event: | Conference | ||||
Location of Event: | Seattle, United States | ||||
Date(s) of Event: | 10–15 Jul 2022 | ||||
Open Access Version: |
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