NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles
NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles
No Thumbnail Available
Files
There are no files associated with this item.
Date
2021
Editors
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
International patent number
Link to the license
EU project number
Project
Open Access publication
Collections
Title in another language
Publication type
Contribution to a conference collection
Publication status
Published
Published in
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume / Merlo, Paola; Tiedemann, Jorg; Tsarfaty, Reut (ed.). - Stroudsburg, PA : Association for Computational Linguistics, 2021. - pp. 1663-1675
Abstract
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).
Summary in another language
Subject (DDC)
320 Politics
Keywords
Conference
16th Conference of the European Chapter of the Association for Computational Linguistics (online), Apr 19, 2021 - Apr 23, 2021
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
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), Apr 19, 2021 - Apr 23, 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, pp. 1663-1675BibTex
@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>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
URL of original publication
Test date of URL
2021-04-30
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
No