Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1530
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dc.contributor.authorCieliebak, Mark-
dc.contributor.authorDeriu, Jan Milan-
dc.contributor.authorEgger, Dominic-
dc.contributor.authorUzdilli, Fatih-
dc.date.accessioned2017-12-14T14:26:16Z-
dc.date.available2017-12-14T14:26:16Z-
dc.date.issued2017-12-11-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1856-
dc.description.abstractIn this paper we present SB10k, a newcorpus for sentiment analysis with approx.10,000 German tweets.We use this new corpus and two existingcorpora to provide state-of-the-art bench-marks for sentiment analysis in German:we implemented a CNN (based on thewinning system of SemEval-2016) anda feature-based SVM and compare theirperformance on all three corpora.For the CNN, we also created Germanword embeddings trained on 300Mtweets. These word embeddings werethen optimized for sentiment analysisusing distant-supervised learning.The new corpus, the German wordembeddings (plain and optimized), andsource code to re-run the benchmarks arepublicly available.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSentiment Analysisde_CH
dc.subjectCorpusde_CH
dc.subjectTwitterde_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc410.285: Computerlinguistikde_CH
dc.titleA Twitter corpus and benchmark resources for german sentiment analysisde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Informationstechnologie (InIT)de_CH
dc.identifier.doi10.18653/v1/W17-1106de_CH
dc.identifier.doi10.21256/zhaw-1530de_CH
zhaw.conference.details5th International Workshop on Natural Language Processing for Social Media, Boston, MA, USA, December 11, 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end51de_CH
zhaw.pages.start45de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSoftware Systemsde_CH
Appears in Collections:Publikationen School of Engineering

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