Abstract
Listening to music can affect people emotions. They can experience simultaneous feelings, such as happiness and hope, or sadness and angry, when a song is being played. However, infering emotions that can be caused by a musical fragment is a complex task. To deduce relationships between feelings and music, we propose a sentiment analysis method based on data mining. In particular, different musical features are extracted and classified to analyze the influence of some music parameters on human emotions. In this process, data mining algorithms such as Random k-Labelsets, Multi-Label k-Nearest Neighbors or Apriori have been essential for the success of our proposal.
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Acknowledgments
This work was supported by the Spanish Ministry, Ministerio de Economía y Competitividad and FEDER funds. SURF: Intelligent System for integrated and sustainable management of urban fleets TIN2015-65515-C4-3-R, and through the FPU program FPU2013 of the Ministry of Education and Culture, number 2071.
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Gómez, L.M., Cáceres, M.N. (2018). Applying Data Mining for Sentiment Analysis in Music. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_20
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