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[research report]

dc.contributor.authorBoland, Katarinade
dc.contributor.authorWira-Alam, Andiasde
dc.contributor.authorMesserschmidt, Reinhardde
dc.date.accessioned2013-04-30T08:11:04Z
dc.date.available2013-04-30T08:11:04Z
dc.date.issued2013de
dc.identifier.issn1868-9051de
dc.identifier.urihttp://www.ssoar.info/ssoar/handle/document/33939
dc.description.abstractThe availability of annotated data is an important prerequisite for the development of machine learning algorithms for sentiment analysis. However, as manually labeling large datasets is time-consuming and expensive, few datasets are available and most of them represent a small sample of a very narrow domain, e.g. movie reviews or reviews of a certain product type. Additionally, many annotated datasets are available for English texts only. However, the influence of different characteristics of the input dataset on the performance of algorithms for sentiment analysis remains unclear if only training data from one specific domain is available or if specific domains are mixed in the test corpus. We therefore introduce a new dataset for German product reviews of various product types and investigate whether even small variances in this specific domain (different product types) already exhibit different characteristics, e.g. with regard to the difficulty of sentiment annotation. The annotation of this corpus lays the basis for future enhanced annotations of similar corpora and for the extension of our annotations to corpora of inherently different domains. These will then serve to investigate the influence of different corpus characteristics on different algorithms for sentiment analysis and as a basis to apply machine learning methods for sentence-wise sentiment analysis for German texts.en
dc.languageende
dc.subject.ddcNaturwissenschaftende
dc.subject.ddcScienceen
dc.titleCreating an Annotated Corpus for Sentiment Analysis of German Product Reviewsde
dc.description.reviewbegutachtetde
dc.description.reviewrevieweden
dc.source.volume2013/05de
dc.publisher.countryDEU
dc.publisher.cityMannheimde
dc.source.seriesGESIS-Technical Reports
dc.subject.classozNaturwissenschaften, Technik(wissenschaften), angewandte Wissenschaftende
dc.subject.classozNatural Science and Engineering, Applied Sciencesen
dc.identifier.urnurn:nbn:de:0168-ssoar-339398
dc.rights.licenceDeposit Licence - Keine Weiterverbreitung, keine Bearbeitungde
dc.rights.licenceDeposit Licence - No Redistribution, No Modificationsen
ssoar.gesis.collectionWGLde
ssoar.contributor.institutionGESISde
internal.statusformal und inhaltlich fertig erschlossende
dc.type.stockmonographde
dc.type.documentForschungsberichtde
dc.type.documentresearch reporten
dc.source.pageinfo16de
internal.identifier.classoz50200
internal.identifier.document12
dc.contributor.corporateeditorGESIS - Leibniz-Institut für Sozialwissenschaften
internal.identifier.corporateeditor133
internal.identifier.ddc500
dc.description.pubstatusVeröffentlichungsversionde
dc.description.pubstatusPublished Versionen
internal.identifier.licence3
internal.identifier.pubstatus1
internal.identifier.review2
internal.identifier.series281
ssoar.wgl.collectiontruede
internal.check.abstractlanguageharmonizerCERTAIN
internal.check.languageharmonizerCERTAIN_RETAINED


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