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

The presentation for our entry to the Linked Data Mining Challenge 2016 organized by Know@LOD workshop at ESWC 2016

Transcript

1. 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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2. “
   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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3. 1.
   Hyphoteses
   What is our intuition?
   

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4. Bands vs.
   Singers
   Bands are more
   successful than single
   artists  rdf:type of
   dbo:artist
   

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5. 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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6. Language
   Albums in English are
   more likely to be
   popular 
   dbo:language
   

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

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8. Reviews
   Words used for good
   albums differ from the
   ones used for bad
   albums
   http://www.youtube.com
   

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9. Award
   winners
   Albums of award winning
   artists are likely to be
   more successful  #
   awards of dbo:artist
   

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10. 2.
    Datasets and Method
    

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11. Datasets
    ◎ Training dataset: 1,280 album URIs
    ◎ Test dataset: 320 album URIs
    ◎ DBpedia
    ◎ Metacritic.com
    

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12. Method
    

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13. 3.
    Experiments and
    Results
    

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14. 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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15. 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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16. Thanks!
    Any questions?
    Find our solution in GitHub
    https://github.com/semihyumusak/KNOW2016
    

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