Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Music and Probability
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Outlining a high level cognitive approach to how we select media based on affective user preferences, we model the latent semantics of lyrics as patterns of emotional components. Using a selection of affective last.fm tags as top-down emotional buoys, we apply LSA latent semantic analysis to bottom-up represent the correlation of terms and song lyrics in a vector space that reflects the emotional context. Analyzing the resulting patterns of affective components, by comparing them against last.fm tag clouds describing the corresponding songs, we propose that it might be feasible to automatically generate affective user preferences based on song lyrics.