Abstract
Lyrics can be used to predict the emotions of songs, and if combined with methods based on audio, better predictions can be achieved. In this paper, we present a new approach to lyric emotion regression. We first build a Latent Dirichlet Allocation (LDA) model from a large corpus of unlabeled lyrics. Based on the model, we can infer the latent topic probabilities of lyrics. Based on the latent topic probabilities of labeled lyrics, we devise a scheme for training and integrating emotion regression models, in which separate models are trained for latent topics and the outputs of those models are combined to get the final regression result. Experimental results show that this scheme can effectively improve the emotion regression accuracy.
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Yang, D., Chen, X., Zhao, Y. (2011). A LDA-Based Approach to Lyric Emotion Regression. In: Wang, Y., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent and Soft Computing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25661-5_43
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DOI: https://doi.org/10.1007/978-3-642-25661-5_43
Publisher Name: Springer, Berlin, Heidelberg
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