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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1526
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dc.contributor.authorvon Grünigen, Dirk-
dc.contributor.authorWeilenmann, Martin-
dc.contributor.authorDeriu, Jan Milan-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2017-12-14T14:16:48Z-
dc.date.available2017-12-14T14:16:48Z-
dc.date.issued2017-
dc.identifier.isbn9781510838710de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1852-
dc.description.abstractIn this paper we investigate the cross-domain performance of sentiment analysis systems. For this purpose we train a convolutional neural network (CNN) on data from different domains and evaluate its performance on other domains. Furthermore, we evaluate the usefulness of combining a large amount of different smaller annotated corpora to a large corpus. Our results show that more sophisticated approaches are required to train a system that works equally well on various domains.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSentiment Analysisde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titlePotential and limitations of cross-domain sentiment classificationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeStroudsburgde_CH
dc.identifier.doi10.18653/v1/W17-1103de_CH
dc.identifier.doi10.21256/zhaw-1526-
zhaw.conference.detailsFifth International Workshop on Natural Language Processing for Social Media, Valencia, Spain, 3-7 April 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end24de_CH
zhaw.pages.start17de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the Fifth International Workshop on Natural Language Processing for Social Mediade_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
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von Grünigen, D., Weilenmann, M., Deriu, J. M., & Cieliebak, M. (2017). Potential and limitations of cross-domain sentiment classification [Conference paper]. Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, 17–24. https://doi.org/10.18653/v1/W17-1103
von Grünigen, D. et al. (2017) ‘Potential and limitations of cross-domain sentiment classification’, in Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media. Stroudsburg: Association for Computational Linguistics, pp. 17–24. Available at: https://doi.org/10.18653/v1/W17-1103.
D. von Grünigen, M. Weilenmann, J. M. Deriu, and M. Cieliebak, “Potential and limitations of cross-domain sentiment classification,” in Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, 2017, pp. 17–24. doi: 10.18653/v1/W17-1103.
VON GRÜNIGEN, Dirk, Martin WEILENMANN, Jan Milan DERIU und Mark CIELIEBAK, 2017. Potential and limitations of cross-domain sentiment classification. In: Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media. Conference paper. Stroudsburg: Association for Computational Linguistics. 2017. S. 17–24. ISBN 9781510838710
von Grünigen, Dirk, Martin Weilenmann, Jan Milan Deriu, and Mark Cieliebak. 2017. “Potential and Limitations of Cross-Domain Sentiment Classification.” Conference paper. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, 17–24. Stroudsburg: Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-1103.
von Grünigen, Dirk, et al. “Potential and Limitations of Cross-Domain Sentiment Classification.” Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, Association for Computational Linguistics, 2017, pp. 17–24, https://doi.org/10.18653/v1/W17-1103.


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