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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1529
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dc.contributor.authorMüller, Simon-
dc.contributor.authorHuonder, Tobias-
dc.contributor.authorDeriu, Jan Milan-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2017-12-14T14:24:50Z-
dc.date.available2017-12-14T14:24:50Z-
dc.date.issued2017-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1855-
dc.description.abstractIn this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN. With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4th place.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSentiment Analysisde_CH
dc.subjectNatural Language Processingde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc410.285: Computerlinguistikde_CH
dc.titleTopicThunder at SemEval-2017 Task 4 : sentiment classification using a convolutional neural network with distant supervisionde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-1529-
zhaw.conference.details11th International Workshop on Semantic Evaluation, Vancouver, Canada, 3-4 August 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end771de_CH
zhaw.pages.start766de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
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Müller, S., Huonder, T., Deriu, J. M., & Cieliebak, M. (2017). TopicThunder at SemEval-2017 Task 4 : sentiment classification using a convolutional neural network with distant supervision [Conference paper]. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 766–771. https://doi.org/10.21256/zhaw-1529
Müller, S. et al. (2017) ‘TopicThunder at SemEval-2017 Task 4 : sentiment classification using a convolutional neural network with distant supervision’, in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Association for Computational Linguistics, pp. 766–771. Available at: https://doi.org/10.21256/zhaw-1529.
S. Müller, T. Huonder, J. M. Deriu, and M. Cieliebak, “TopicThunder at SemEval-2017 Task 4 : sentiment classification using a convolutional neural network with distant supervision,” in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 2017, pp. 766–771. doi: 10.21256/zhaw-1529.
MÜLLER, Simon, Tobias HUONDER, Jan Milan DERIU und Mark CIELIEBAK, 2017. TopicThunder at SemEval-2017 Task 4 : sentiment classification using a convolutional neural network with distant supervision. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Conference paper. Association for Computational Linguistics. 2017. S. 766–771
Müller, Simon, Tobias Huonder, Jan Milan Deriu, and Mark Cieliebak. 2017. “TopicThunder at SemEval-2017 Task 4 : Sentiment Classification Using a Convolutional Neural Network with Distant Supervision.” Conference paper. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 766–71. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-1529.
Müller, Simon, et al. “TopicThunder at SemEval-2017 Task 4 : Sentiment Classification Using a Convolutional Neural Network with Distant Supervision.” Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Association for Computational Linguistics, 2017, pp. 766–71, https://doi.org/10.21256/zhaw-1529.


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