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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3779
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dc.contributor.authorDürr, Oliver-
dc.contributor.authorUzdilli, Fatih-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2018-06-26T14:41:44Z-
dc.date.available2018-06-26T14:41:44Z-
dc.date.issued2014-
dc.identifier.isbn978-1-941643-24-2de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/7363-
dc.description.abstractIn this paper, we describe how we created a meta-classifier to detect the message-level sentiment of tweets. We participated in SemEval-2014 Task 9B by combining the results of several existing classifiers using a random forest. The results of 5 other teams from the competition as well as from 7 general purpose commercial classifiers were used to train the algorithm. This way, we were able to get a boost of up to 3.24 F1 score points.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectSentiment analysisde_CH
dc.subjectRandom forestde_CH
dc.subjectCompetitionde_CH
dc.subjectEnsemble methodde_CH
dc.subject.ddc410.285: Computerlinguistikde_CH
dc.titleJOINT_FORCES : unite competing sentiment classifiers with random forestde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.21256/zhaw-3779-
zhaw.conference.detailsInternational Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end369de_CH
zhaw.pages.start366de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsProceedings of the International Workshop on Semantic Evaluation (SemEval-2014)de_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
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Dürr, O., Uzdilli, F., & Cieliebak, M. (2014). JOINT_FORCES : unite competing sentiment classifiers with random forest [Conference paper]. Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 366–369. https://doi.org/10.21256/zhaw-3779
Dürr, O., Uzdilli, F. and Cieliebak, M. (2014) ‘JOINT_FORCES : unite competing sentiment classifiers with random forest’, in Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014). Association for Computational Linguistics, pp. 366–369. Available at: https://doi.org/10.21256/zhaw-3779.
O. Dürr, F. Uzdilli, and M. Cieliebak, “JOINT_FORCES : unite competing sentiment classifiers with random forest,” in Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 2014, pp. 366–369. doi: 10.21256/zhaw-3779.
DÜRR, Oliver, Fatih UZDILLI und Mark CIELIEBAK, 2014. JOINT_FORCES : unite competing sentiment classifiers with random forest. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014). Conference paper. Association for Computational Linguistics. 2014. S. 366–369. ISBN 978-1-941643-24-2
Dürr, Oliver, Fatih Uzdilli, and Mark Cieliebak. 2014. “JOINT_FORCES : Unite Competing Sentiment Classifiers with Random Forest.” Conference paper. In Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), 366–69. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-3779.
Dürr, Oliver, et al. “JOINT_FORCES : Unite Competing Sentiment Classifiers with Random Forest.” Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014), Association for Computational Linguistics, 2014, pp. 366–69, https://doi.org/10.21256/zhaw-3779.


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