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dc.contributor.authorYumusak, Semih
dc.contributor.authorMuñoz, Emir
dc.contributor.authorMinervini, Pasquale
dc.contributor.authorDogdu, Erdogan
dc.contributor.authorKodaz, Halife
dc.date.accessioned2016-09-15T13:21:50Z
dc.date.available2016-09-15T13:21:50Z
dc.date.issued2016
dc.identifier.citationYumusak, Semih, Muñoz, Emir, Minervini, Pasquale, Dogdu, Erdogan, & Kodaz, Halife. (2016). A hybrid method for rating prediction using linked data features and text reviews. Paper presented at the Know@LOD 2015, 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC 2015), Portoroz, Slovenia. http://ceur-ws.org/Vol-1586/ldmc3.pdfen_IE
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/10379/6024
dc.description.abstractThis paper describes our entry for the Linked Data Mining Challenge 2016, which poses the problem of classifying music albums as good or bad by mining Linked Data. The original labels are assigned according to aggregated critic scores published by the Metacritic s website. To this end, the challenge provides datasets that contain the DBpedia reference for music albums. Our approach benefits from Linked Data (LD) and free text to extract meaningful features that help to separate these two classes of music albums. Thus, our features can be summarized as follows: (1) direct object LD features, (2) aggregated count LD features, and (3) textual review features. We filtered out those properties somehow related with scores and Metacritic to build unbiased models. By using these sets of features, we trained seven models using 10-fold cross validation to estimate performance. We reached the best average accuracy of 87.81% in the training data using a Linear SVM model and all our features, while we reached 90% in the testing data.en_IE
dc.description.sponsorshipThis research is partly supported by The Scientific and Technological Research Council of Turkey (Ref.No: B.14.2. TBT.0.06.01-21514107-020-155998)en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherCEUR-WS.orgen_IE
dc.relation.ispartofKnow@LODen
dc.subjectData analyticsen_IE
dc.subjectLinked dataen_IE
dc.subjectSPARQLen_IE
dc.subjectClassificationen_IE
dc.subjectMachine learningen_IE
dc.subject#Know@LOD2016en_IE
dc.titleA hybrid method for rating prediction using linked data features and text reviewsen_IE
dc.typeWorkshop paperen_IE
dc.date.updated2016-09-15T11:12:31Z
dc.local.publishedsourcehttp://ceur-ws.org/Vol-1586/ldmc3.pdfen_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|1267880|~|
dc.internal.rssid11398954
dc.local.contactEmir Munoz, Deri, Ida Business Park, Lower Dangan, Nui Galway. - Email: e.munoz1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
nui.item.downloads137


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