Publikation:

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

Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2021

Autor:innen

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung

Sammlungen

Politik- und Verwaltungswissenschaft: Publikationen
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

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).

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
320 Politik

Schlagwörter

Konferenz

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

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Verknüpfte Datensätze

Zitieren

ISO 690HAMBORG, 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}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/53815">
    <dc:contributor>Donnay, Karsten</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-05-28T13:22:35Z</dc:date>
    <dcterms:title>NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles</dcterms:title>
    <dcterms:issued>2021</dcterms:issued>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53815"/>
    <dc:rights>terms-of-use</dc:rights>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Hamborg, Felix</dc:creator>
    <dc:creator>Donnay, Karsten</dc:creator>
    <dcterms:abstract xml:lang="eng">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).</dcterms:abstract>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Hamborg, Felix</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-05-28T13:22:35Z</dcterms:available>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.
https://www.aclweb.org/anthology/2021.eacl-main.142/

Prüfdatum der URL

2021-04-30

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Diese Publikation teilen