Abstract
In recent years, efficient and intelligent music information retrieval became very important. One essential aspect of this field is music emotion classification by earning from lyrics. This problem is different from traditional text categorization in that more linguistic or semantic information is required for better emotion analysis. Therefore, we focus on how to extract useful and meaningful language features in this paper. We investigate three kinds of preprocessing methods and a series of language grams having different n-order under the well-known n-gram language model framework to extract more semantic features. Then, we employ three supervised learning methods (Naïve Bayes, maximum entropy classification, and support vector machines) to examine the classification performance. Experimental results show that feature extraction methods improve music emotion classification accuracies. Maximum entropy classification with unigram+bigram+trigram gets best accuracy and it is suitable for music emotion classification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huron, D.: Perceptual and Cognitive Applications in Music Information Retrieval. In: Proc. Int. Symp. Music Information Retrieval (2000)
Li, T., Ogihara, M.: Detecting Emotion in Music. In: Proc. Fifth Int. Symp. Music Information Retrieval (ISMIR 2003), pp. 239–240 (2003)
Li, T., Ogihara, M.: Content-based Music Similarity Search and Emotion Detection. In: Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, pp. 705–708 (2004)
Li, T., Ogihara, M.: Toward Intelligent Music Information Retrieval. IEEE Transactions on Multimedia 8(3), 564–574 (2006)
Skowronek, J., McKinney, M., van de Par, S.: A Demonstrator for Automatic Music Mood Estimation. In: Proc. 8th Int. Symp. Music Information Retrieval (ISMIR 2007) (2007)
Scott, S., Matwin, S.: Text Classification Using WordNet Hypernyms. In: COLING-ACL 1998 Workshop, pp. 38–44 (1998)
Hevner, K.: Experimental Studies of the Elements of Expression in Music. Amer. J. Psychol. 48, 246–268 (1936)
Lu, L., Liu, D., Zhang, H.: Automatic Mood Detection and Tracking of Music Audio Signals. IEEE transactions on audio, speech, and language processing 14(1), 5–18 (2006)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification Using Machine Learning Techniques. In: Proceedings of the Conference on Empircal Methods in Natural Language Processing, Philadelphia, US, pp. 79–86 (2002)
Pang, B., Lee, L.: A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cus. In: Proceedings of 42nd Meeting of the Association for Computational Linguistics, Barcelona, ES, pp. 271–278 (2004)
Mccallum, A., Nigam, K.: A Comparison of Event Models for Naïve Bayes Text Classification. In: Proceedings of AAAI 1998 Workshop on Learning for Text Categorization, pp. 41–48 (1998)
Rennie, J.D.M., Shih, L., Teevan, J., Karger, D.R.: Tackling the Poor Assumption of Naïve Bayes Text Classifiers. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003) (2003)
Berger, A.L., Della Pietra, S.A., Della Pietra, V.J.: A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics 22(1), 39–71 (1996)
Vapnik, V.: Principles of Risk Minimization for Learning Theory. In: Lippman, D.S., Moody, J.E., Touretzky, D.S. (eds.) Advances in Neural Information Processing Systems, pp. 831–838. Morgan Kaufmann, San Francisco (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, H., Jin, J., Xiong, Y., Chen, B., Sun, W., Zhao, L. (2008). Language Feature Mining for Music Emotion Classification via Supervised Learning from Lyrics. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-92137-0_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92136-3
Online ISBN: 978-3-540-92137-0
eBook Packages: Computer ScienceComputer Science (R0)