Exploring implicit sentiment evoked by fine-grained news events
- Author
- Cynthia Van Hee (UGent) , Orphée De Clercq (UGent) and Veronique Hoste (UGent)
- Organization
- Abstract
- We investigate the feasibility of defining sentiment evoked by fine-grained news events. Our research question is based on the premise that methods for detecting implicit sentiment in news can be a key driver of content diversity, which is one way to mitigate the detrimental effects of filter bubbles that recommenders based on collaborative filtering may produce. Our experiments are based on 1,735 news articles from major Flemish newspapers that were manually annotated, with high agreement, for implicit sentiment. While lexical resources prove insufficient for sentiment analysis in this data genre, our results demonstrate that machine learning models based on SVM and BERT are able to automatically infer the implicit sentiment evoked by news events.
- Keywords
- lt3, news dna
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8704637
- MLA
- Van Hee, Cynthia, et al. “Exploring Implicit Sentiment Evoked by Fine-Grained News Events.” Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021), Association for Computational Linguistics, 2021, pp. 138–48.
- APA
- Van Hee, C., De Clercq, O., & Hoste, V. (2021). Exploring implicit sentiment evoked by fine-grained news events. Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021), 138–148. Association for Computational Linguistics.
- Chicago author-date
- Van Hee, Cynthia, Orphée De Clercq, and Veronique Hoste. 2021. “Exploring Implicit Sentiment Evoked by Fine-Grained News Events.” In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021), 138–48. Association for Computational Linguistics.
- Chicago author-date (all authors)
- Van Hee, Cynthia, Orphée De Clercq, and Veronique Hoste. 2021. “Exploring Implicit Sentiment Evoked by Fine-Grained News Events.” In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021), 138–148. Association for Computational Linguistics.
- Vancouver
- 1.Van Hee C, De Clercq O, Hoste V. Exploring implicit sentiment evoked by fine-grained news events. In: Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021). Association for Computational Linguistics; 2021. p. 138–48.
- IEEE
- [1]C. Van Hee, O. De Clercq, and V. Hoste, “Exploring implicit sentiment evoked by fine-grained news events,” in Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021), Online, 2021, pp. 138–148.
@inproceedings{8704637,
abstract = {{We investigate the feasibility of defining sentiment evoked by fine-grained news events. Our research question is based on the premise that methods for detecting implicit sentiment in news can be a key driver of content diversity, which is one way to mitigate the detrimental effects of filter bubbles that recommenders based on collaborative filtering may produce. Our experiments are based on 1,735 news articles from major Flemish newspapers that were manually annotated, with high agreement, for implicit sentiment. While lexical resources prove insufficient for sentiment analysis in this data genre, our results demonstrate that machine learning models based on SVM and BERT are able to automatically infer the implicit sentiment evoked by news events.}},
author = {{Van Hee, Cynthia and De Clercq, Orphée and Hoste, Veronique}},
booktitle = {{Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021)}},
isbn = {{9781954085183}},
keywords = {{lt3,news dna}},
language = {{eng}},
location = {{Online}},
pages = {{138--148}},
publisher = {{Association for Computational Linguistics}},
title = {{Exploring implicit sentiment evoked by fine-grained news events}},
url = {{https://aclanthology.org/2021.wassa-1.15}},
year = {{2021}},
}