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Title: A hybrid method for rating prediction using Linked Data features and text reviews
Authors: Yumuşak, S.
Muñoz, E.
Minervini, P.
Doğdu, Erdoğan
Kodaz, H.
Keywords: #Know@LOD2016
Linked data
Machine learning
Issue Date: 2016
Publisher: CEUR-WS
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, 30 May 2016, , 122147
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.
ISSN: 1613-0073
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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