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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-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1856-
dc.description.abstractIn this paper we present SB10k, a new corpus for sentiment analysis with approx.10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art bench-marks for sentiment analysis in German:we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly 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.ddc006: Spezielle Computerverfahrende_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 Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-1530-
dc.identifier.doi10.18653/v1/W17-1106de_CH
zhaw.conference.details5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 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
zhaw.webfeedNatural Language Processingde_CH
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Cieliebak, M., Deriu, J. M., Egger, D., & Uzdilli, F. (2017). A Twitter corpus and benchmark resources for german sentiment analysis [Conference paper]. 5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017, 45–51. https://doi.org/10.21256/zhaw-1530
Cieliebak, M. et al. (2017) ‘A Twitter corpus and benchmark resources for german sentiment analysis’, in 5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017. Association for Computational Linguistics, pp. 45–51. Available at: https://doi.org/10.21256/zhaw-1530.
M. Cieliebak, J. M. Deriu, D. Egger, and F. Uzdilli, “A Twitter corpus and benchmark resources for german sentiment analysis,” in 5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017, 2017, pp. 45–51. doi: 10.21256/zhaw-1530.
CIELIEBAK, Mark, Jan Milan DERIU, Dominic EGGER und Fatih UZDILLI, 2017. A Twitter corpus and benchmark resources for german sentiment analysis. In: 5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017. Conference paper. Association for Computational Linguistics. 2017. S. 45–51
Cieliebak, Mark, Jan Milan Deriu, Dominic Egger, and Fatih Uzdilli. 2017. “A Twitter Corpus and Benchmark Resources for German Sentiment Analysis.” Conference paper. In 5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017, 45–51. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-1530.
Cieliebak, Mark, et al. “A Twitter Corpus and Benchmark Resources for German Sentiment Analysis.” 5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017, Association for Computational Linguistics, 2017, pp. 45–51, https://doi.org/10.21256/zhaw-1530.


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