Abstract
The increasing amounts of media becoming available in converged
digital broadcast and mobile broadband networks will require intelligent interfaces capable of personalizing
the selection of content. Aiming to capture the mood in the content, we construct a semantic space based on tags,
frequently used to describe emotions associated with music in the last.fm social network. Implementing latent semantic analysis (LSA), we model the affective context of songs based on their lyrics, and apply a similar approach to extract
moods from BBC synopsis descriptions of TV episodes using TV-Anytime atmosphere terms. Based on our early results,
we propose that LSA could be implemented as machinelearning method to extract emotional context and model
affective user preferences.