Multi-Label Classification of Movie Genres using Text-based Features and WordNet Hypernyms
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2010-06-18
Language
en
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Abstract
Text 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.
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Faculteit der Sociale Wetenschappen