NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

Hamborg, Felix; Donnay, Karsten

NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2021

Autor:innen

Hamborg, Felix

Donnay, Karsten

Sammlungen

Politik- und Verwaltungswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

MERLO, Paola, ed., Jorg TIEDEMANN, ed., Reut TSARFATY, ed.. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA: Association for Computational Linguistics, 2021, pp. 1663-1675

Zusammenfassung

Previous research on target-dependent sentiment classification (TSC) has mostly focused on reviews, social media, and other domains where authors tend to express sentiment explicitly. In this paper, we investigate TSC in news articles, a much less researched TSC domain despite the importance of news as an essential information source in individual and societal decision making. We introduce NewsMTSC, a high-quality dataset for TSC on news articles with key differences compared to established TSC datasets, including, for example, different means to express sentiment, longer texts, and a second test-set to measure the influence of multi-target sentences. We also propose a model that uses a BiGRU to interact with multiple embeddings, e.g., from a language model and external knowledge sources. The proposed model improves the performance of the prior state-of-the-art from F1_m=81.7 to 83.1 (real-world sentiment distribution) and from F1_m=81.2 to 82.5 (multi-target sentences).

Fachgebiet (DDC)

320 Politik

Konferenz

16th Conference of the European Chapter of the Association for Computational Linguistics (online), 19. Apr. 2021 - 23. Apr. 2021

Zitieren

ISO 690

HAMBORG, Felix, Karsten DONNAY, 2021. NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles. 16th Conference of the European Chapter of the Association for Computational Linguistics (online), 19. Apr. 2021 - 23. Apr. 2021. In: MERLO, Paola, ed., Jorg TIEDEMANN, ed., Reut TSARFATY, ed.. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA: Association for Computational Linguistics, 2021, pp. 1663-1675

BibTex

@inproceedings{Hamborg2021NewsM-53815,
  year={2021},
  title={NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles},
  url={https://www.aclweb.org/anthology/2021.eacl-main.142/},
  publisher={Association for Computational Linguistics},
  address={Stroudsburg, PA},
  booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  pages={1663--1675},
  editor={Merlo, Paola and Tiedemann, Jorg and Tsarfaty, Reut},
  author={Hamborg, Felix and Donnay, Karsten}
}

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URL der Originalveröffentl.

https://www.aclweb.org/anthology/2021.eacl-main.142/

Prüfdatum der URL

2021-04-30

Universitätsbibliographie

Nein