  The classification of television content helps users organise and navigate
through the large list of channels and programs now available. In this paper,
we address the problem of television content classification by exploiting text
information extracted from program transcriptions. We present an analysis which
adapts a model for sentiment that has been widely and successfully applied in
other fields such as music or blog posts. We use a real-world dataset obtained
from the Boxfish API to compare the performance of classifiers trained on a
number of different feature sets. Our experiments show that, over a large
collection of television content, program genres can be represented in a
three-dimensional space of valence, arousal and dominance, and that promising
classification results can be achieved using features based on this
representation. This finding supports the use of the proposed representation of
television content as a feature space for similarity computation and
recommendation generation.
