Keyword-based Approach for Lyrics Emotion Variation Detection

Ricardo Malheiro, Hugo Gonçalo Oliveira, Paulo Gomes, Rui Pedro Paiva


This research addresses the role of the lyrics in the context of music emotion variation detection. To accomplish this task we create a system to detect the predominant emotion expressed by each sentence (verse) of the lyrics. The system employs Russell’s emotion model and contains 4 sets of emotions associated to each quadrant. To detect the predominant emotion in each verse, we propose a novel keyword-based approach, which receives a sentence (verse) and classifies it in the appropriate quadrant. To tune the system parameters, we created a 129-sentence training dataset from 68 songs. To validate our system, we created a separate ground-truth containing 239 sentences (verses) from 44 songs annotated manually with an average of 7 annotations per sentence. The system attains 67.4% F-Measure score.


  1. Agrawal, A., An, A., 2012. Unsupervised Emotion Detection from Text using Semantic and Syntactic Relations. In Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, 346-353.
  2. Aman, S., Szpakowicz, S. 2007. Identifying Expressions of Emotion in Text. In Proceedings 10th International Conference on Text, Speech and Dialogue TSD 2007, Plzen, Czech Republic, Lecture Notes in Computer Science 4629, Springer, pp. 196-205.
  3. Besson, M., Faita, F., Peretz, I., Bonnel, A., Requin, J. 1998. Singing in the brain: Independence of lyrics and tunes, Psychological Science, 9.
  4. Binali, H., Wu, C., Potdar, V. 2010. Computational Approaches for Emotion Detection in Text. 4th IEEE International Conference on Digital Ecosystems and Technologies.
  5. Bradley, M., Lang, P. 1999. Affective Norms for English Words (ANEW): Stimuli, Instruction Manual and Affective Ratings. Technical report C-1, The Center for Research in Psychophysiology, University of Florida.
  6. Chopade, C. 2015. Text based Emotion Recognition. International Journal of Science and Research (IJSR), 4(6), 409-414.
  7. Chunling, M., Prendinger, H., Ishizuka, M. 2005. Emotion Estimation and Reasoning Based on Affective Textual Interaction. In Affective Computing and Intelligent Interaction, Vol. 3784/2005: Springer Berlin / Heidelberg, pp. 622-628.
  8. Fontaine, J., Scherer, K., Soriano, C. 2013. Components of Emotional Meaning. A Sourcebook. Oxford University Press.
  9. Hancock, J., Landrigan, C., Silver, C. 2007. Expressing emotions in text-based communication. In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 929-932.
  10. Hevner, K. 1936. Experimental studies of the elements of expression in music. American Journal of Psychology, 48: 246-268.
  11. Hu, Y., Chen, X., Yang, D. 2009. Lyric-Based Song Emotion Detection with Affective Lexicon and Fuzzy Clustering Method. Tenth Int. Society for Music Information Retrieval Conference.
  12. Hu, X., Downie, J. 2010. Improving mood classification in music digital libraries by combining lyrics and audio. Proc. Tenth Ann. joint conf. on Digital libraries, pp. 159-168.
  13. Juslin, P., Laukka, P. 2004. Expression, Perception, and Induction of Musical Emotions: A Review and a Questionnaire Study of Everyday Listening. Journal of New Music Research, 33 (3), 217-238.
  14. Kao, E., Chun-Chieh, L., Ting-Hao, Y., Chang-Tai, H., Von-Wun, S. 2009. Towards Text-based Emotion Detection. In International Conference on Information Management and Engineering, pp. 70-74.
  15. Korhonen, M., Clausi, D., Jernigan, M. 2006. Modeling emotional content of music using system identification. IEEE Transactions Systems Man Cyber, 36(3), 588- 599.
  16. Krippendorff, K. 2004. Content Analysis: An Introduction to its Methodology. 2nd edition, chapter 11. Sage, Thousand Oaks, CA.
  17. Landis, J., Koch, G. 1977. The measurement of observer agreement for categorical data. Biometrics, 33:159- 174.
  18. Laurier, C., Grivolla, J., Herrera, P. 2008. Multimodal music mood classification using audio and lyrics. Proc. of the Int. Conf. on Machine Learning and Applications.
  19. Li, H., Pang, N., Guo, S. 2007. Research on Textual Emotion Recognition Incorporating Personality Factor. In International conference on Robotics and Biomimetics, Sanya, China.
  20. Lu, C., Hong, J-S., Cruz-Lara, S. 2006. Emotion Detection in Textual Information by Semantic Role Labeling and Web Mining Techniques. Third Taiwanese-French Conf. on Information Technology.
  21. Malheiro, R., Panda, R, Gomes, P., Paiva, R. 2016. Emotionally-Relevant Features for Classification and Regression of Music Lyrics. IEEE Transactions on Journal Affective Computing, Vol 8.
  22. Miller, G. 1995. WordNet: A Lexical Database for English Communications of the ACM Vol. 38, No 11: 39-41.
  23. Russell, J. 1980. A circumspect model of affect. Journal of Psychology and Social Psychology, vol. 39, no. 6, p. 1161.
  24. Russell, J. 2003. Core affect and the psychological construction of emotion. Psychology Review, 110, 1, 145-172.
  25. Schmidt, E., Turnbull, D., Kim, Y. 2010. Feature selection for content-based, time-varying musical emotion regression. In Proceedings of the ACM International Conference on Multimedia Information Retrieval, 267- 274.
  26. Schubert, E., 1999. Measurement and time series analysis of emotion in music. Ph.D. dissertion, School of Music Education, University of New South Wales, Sydney, Australia.
  27. Strapparava, C., Valitutti, A. 2004. Wordnet-affect: an affective extension of wordnet. In Proceedings of the 4th International Conference on Language Resources and Evaluation, pp. 1083-1086, Lisbon.
  28. Taylor, A., Marcus, M., Santorini, B. 2003. The Penn Treebank: an overview. Series Text, Speech and Language Technology. Ch1. 20, 5-22.
  29. Vignoli, F. 2004. Digital Music Interaction concepts: a user study. Proc. of the 5th Int. Conference on Music Information Retrieval.
  30. Whissell, C., 1989. Dictionary of Affect in Language. In Plutchik and Kellerman Emotion: Theory, Research and Experience, vol 4, pp. 113-131, Academic Press, NY.
  31. Yang, Y-H., Liu, C., Chen, H. 2006. Music emotion classification: A fuzzy approach. In Proceedings of the ACM International Conference on Multimedia, 81-84.
  32. Yang, C., Lin, K., Chen, H. 2007. Emotion Classification Using Web Blog Corpora. In IEEE/WIC/ACM International Conference on Web Intelligence.
  33. Yang Y-H. Chen H. 2012. Machine recognition of music emotion: a review. In ACM Transactions on Intelligent Systems and Technology (TIST). Vol. 3, Issue 3.

Paper Citation

in Harvard Style

Malheiro R., Oliveira H., Gomes P. and Paiva R. (2016). Keyword-based Approach for Lyrics Emotion Variation Detection . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 33-44. DOI: 10.5220/0006037300330044

in Bibtex Style

author={Ricardo Malheiro and Hugo Gonçalo Oliveira and Paulo Gomes and Rui Pedro Paiva},
title={Keyword-based Approach for Lyrics Emotion Variation Detection},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},

in EndNote Style

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Keyword-based Approach for Lyrics Emotion Variation Detection
SN - 978-989-758-203-5
AU - Malheiro R.
AU - Oliveira H.
AU - Gomes P.
AU - Paiva R.
PY - 2016
SP - 33
EP - 44
DO - 10.5220/0006037300330044