A Hybrid Method for Rating Prediction Using Linked Data Features and Text Reviews

This 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 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 distinguishing between 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. To build unbiased models, we filtered out those properties somehow related with scores and Metacritic. By using these sets of features, we trained seven models using 10-fold cross-validation to estimate accuracy. 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.

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Eser Adı
(dc.title)
A Hybrid Method for Rating Prediction Using Linked Data Features and Text Reviews
Yayın Türü
(dc.type)
Konferans Bildirisi
Yazar/lar
(dc.contributor.author)
YUMUŞAK, Semih
Yazar/lar
(dc.contributor.author)
MUÑOZ, Emir
Yazar/lar
(dc.contributor.author)
MİNERVİNİ, Pasquale
Yazar/lar
(dc.contributor.author)
DOĞDU, Erdoğan
Yazar/lar
(dc.contributor.author)
KODAZ, Halife
Atıf Dizini
(dc.source.database)
Scopus
Konu Başlıkları
(dc.subject)
Linked Data
Konu Başlıkları
(dc.subject)
#Know@LOD2016
Konu Başlıkları
(dc.subject)
Machine Learning
Konu Başlıkları
(dc.subject)
Classification
Konu Başlıkları
(dc.subject)
SPARQL
Yayıncı
(dc.publisher)
CEUR-WS
Yayın Tarihi
(dc.date.issued)
2016
Kayıt Giriş Tarihi
(dc.date.accessioned)
2020-08-07T12:58:49Z
Açık Erişim tarihi
(dc.date.available)
2020-08-07T12:58:49Z
Kaynak
(dc.source)
5th Joint Workshop on Data Mining and Knowledge Discovery meets Linked Open Data and the 1st International Workshop on Completing and Debugging the Semantic Web, Know@LOD 2016 and CoDeS 2016
ISSN
(dc.identifier.issn)
16130073 (ISSN)
Özet
(dc.description.abstract)
This 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 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 distinguishing between 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. To build unbiased models, we filtered out those properties somehow related with scores and Metacritic. By using these sets of features, we trained seven models using 10-fold cross-validation to estimate accuracy. 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.
Yayın Dili
(dc.language.iso)
en
Tek Biçim Adres
(dc.identifier.uri)
http://hdl.handle.net/20.500.12498/3072
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6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve cerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

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