Multi-Label Classification of Movie Genres using Text-based Features and WordNet Hypernyms

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dc.contributor.advisorGrootjen, F.A.
dc.contributor.authorvan der Meer, S.T.
dc.date.issued2010-06-18
dc.description.abstractText categorization techniques have become increasingly more important in the past decade. Whereas many approaches rely on video or audio features for classifying digital media, text-based features provide a considerable amount of information and are computationally inexpensive to process. In this thesis we present a large movie subtitle database of data in natural language, which will be used to predict genre labels in a multi-label classification problem. We provide methods to extract text-based features and reduce attribute dimensionality effectively. We also demonstrate the generation of a second dataset using WordNet, where all words from the original subtitles are replaced by their direct hypernyms. A final distinction is made within datasets to include TF-IDF-transformations or not. We hypothesize that the dataset containing hypernyms will outperform the original dataset of textbased features. Furthermore, we hypothesize that TF-IDF-transformation has a positive effect on classification accuracy. A selection of multi-label classification techniques were tested on their performance using the four conditions. Results show very good scores on classification performance but no significant difference between the four experimental conditions.en_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/12560
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleMulti-Label Classification of Movie Genres using Text-based Features and WordNet Hypernymsen_US
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