1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCieliebak, Mark-
dc.contributor.authorDürr, Oliver-
dc.contributor.authorUzdilli, Fatih-
dc.date.accessioned2018-06-26T14:38:16Z-
dc.date.available2018-06-26T14:38:16Z-
dc.date.issued2014-
dc.identifier.isbn978-1-63266-621-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/7360-
dc.description.abstractIn this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60%. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectOpinion miningde_CH
dc.subjectMachine learningde_CH
dc.subjectSentiment analysisde_CH
dc.subjectCorpus analyticsde_CH
dc.subject.ddc410.285: Computerlinguistikde_CH
dc.titleMeta-classifiers easily improve commercial sentiment detection toolsde_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
zhaw.conference.details9th International Conference on Language Resources and Evaluation, Reykjavik, Iceland, 26-31 May 2014de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end3947de_CH
zhaw.pages.start3943de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsProceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014)de_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
There are no files associated with this item.
Show simple item record
Cieliebak, M., Dürr, O., & Uzdilli, F. (2014). Meta-classifiers easily improve commercial sentiment detection tools [Conference paper]. Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 3943–3947.
Cieliebak, M., Dürr, O. and Uzdilli, F. (2014) ‘Meta-classifiers easily improve commercial sentiment detection tools’, in Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). Association for Computational Linguistics, pp. 3943–3947.
M. Cieliebak, O. Dürr, and F. Uzdilli, “Meta-classifiers easily improve commercial sentiment detection tools,” in Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 2014, pp. 3943–3947.
CIELIEBAK, Mark, Oliver DÜRR und Fatih UZDILLI, 2014. Meta-classifiers easily improve commercial sentiment detection tools. In: Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). Conference paper. Association for Computational Linguistics. 2014. S. 3943–3947. ISBN 978-1-63266-621-5
Cieliebak, Mark, Oliver Dürr, and Fatih Uzdilli. 2014. “Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.” Conference paper. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 3943–47. Association for Computational Linguistics.
Cieliebak, Mark, et al. “Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.” Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), Association for Computational Linguistics, 2014, pp. 3943–47.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.