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

Transcript

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   A Hybrid Method for Rating Prediction Using Linked Data Features

   and Text Reviews Semih, Y., Emir M., Pasquale, M., Erdogan, D., Halife, K. Linked Data Mining Challenge 2016 - Know@LOD ESWC 2016, Heraklion, Crete, Greece
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   “ What makes a good/bad album of music? Can Linked

   Open Data help with the classification of music albums as “good” or “bad”?
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   1. Hyphoteses What is our intuition?

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   Bands vs. Singers Bands are more successful than single artists

    rdf:type of dbo:artist
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   Music genres Some genres are more popular than others 

   dbo:genre http://hpo.org/two-things-you-need-to-know-about-genre-hopping/
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   Language Albums in English are more likely to be popular

    dbo:language
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   Runtime Longer albums tend to be more popular  dbo:runtime

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   Reviews Words used for good albums differ from the ones

   used for bad albums http://www.youtube.com
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   Award winners Albums of award winning artists are likely to

   be more successful  # awards of dbo:artist
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    2. Datasets and Method

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    Datasets ◎ Training dataset: 1,280 album URIs ◎ Test dataset:

    320 album URIs ◎ DBpedia ◎ Metacritic.com
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    Method

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    3. Experiments and Results

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    Experimental Setup ◎ Python 3.5.1 ◎ Beautiful Soup Library 4.4.0

    (Web Scraping) ◎ scikit-learn Library 0.17 (Data Mining) ◎ Jena Fuseki (LD Caching)
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    Different Classifiers and Different Feature Sets Feature Set Linear SVM

    KNN RBF SVM Dec. Tree Rand. Forest AdaBoost Naïve Bayes LD 76.64% 60.47% 48.05% 72.66% 53.91% 75.00% 76.41% LDA 54.53% 52.58% 54.69% 54.45% 48.91% 54.53% 52.89% LD+LDA 76.72% 60.23% 48.05% 72.66% 52.34% 75.00% 76.41% TEXT 85.00% 50.00% 47.27% 67.27% 52.81% 78.91% 68.44% LD+LDA+TEXT 87.81% 52.81% 47.27% 72.03% 52.58% 82.50% 77.19% + = 90% test set
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    Thanks! Any questions? Find our solution in GitHub https://github.com/semihyumusak/KNOW2016