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dc.contributor.authorCieliebak, Mark-
dc.contributor.authorDürr, Oliver-
dc.contributor.authorUzdilli, Fatih-
dc.date.accessioned2018-06-20T14:37:12Z-
dc.date.available2018-06-20T14:37:12Z-
dc.date.issued2013-
dc.identifier.issn1613-0073de_CH
dc.identifier.otherurn:nbn:de:0074-1096-7de_CH
dc.identifier.urihttp://ceur-ws.org/Vol-1096/paper4.pdfde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/7121-
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. In addition to the quality analysis (measured by various metrics), we also investigate the effect of increasing text length on the performance. Finally, we show that combining all tools using machine learning techniques increases the overall performance significantly.de_CH
dc.language.isoende_CH
dc.publisherRWTH Aachende_CH
dc.relation.ispartofseriesCEUR Workshop Proceedingsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCorpus analyticsde_CH
dc.subjectMachine learningde_CH
dc.subjectSentiment detectionde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titlePotential and limitations of 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.detailsFirst International Workshop on Emotion and Sentiment in Social and Expressive Media (ESSEM 2013), Turin, Italy, 3 December 2013de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end58de_CH
zhaw.pages.start47de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number1096de_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsProceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
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Cieliebak, M., Dürr, O., & Uzdilli, F. (2013). Potential and limitations of commercial sentiment detection tools [Conference paper]. Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), 47–58. http://ceur-ws.org/Vol-1096/paper4.pdf
Cieliebak, M., Dürr, O. and Uzdilli, F. (2013) ‘Potential and limitations of commercial sentiment detection tools’, in Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013). RWTH Aachen, pp. 47–58. Available at: http://ceur-ws.org/Vol-1096/paper4.pdf.
M. Cieliebak, O. Dürr, and F. Uzdilli, “Potential and limitations of commercial sentiment detection tools,” in Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013), 2013, pp. 47–58. [Online]. Available: http://ceur-ws.org/Vol-1096/paper4.pdf
CIELIEBAK, Mark, Oliver DÜRR und Fatih UZDILLI, 2013. Potential and limitations of commercial sentiment detection tools. In: Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013) [online]. Conference paper. RWTH Aachen. 2013. S. 47–58. Verfügbar unter: http://ceur-ws.org/Vol-1096/paper4.pdf
Cieliebak, Mark, Oliver Dürr, and Fatih Uzdilli. 2013. “Potential and Limitations of Commercial Sentiment Detection Tools.” Conference paper. In Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), 47–58. RWTH Aachen. http://ceur-ws.org/Vol-1096/paper4.pdf.
Cieliebak, Mark, et al. “Potential and Limitations of Commercial Sentiment Detection Tools.” Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), RWTH Aachen, 2013, pp. 47–58, http://ceur-ws.org/Vol-1096/paper4.pdf.


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