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Music feeling classification based on lyrics using weighting of non-emotional words

Published: 11 December 2015 Publication History

Abstract

Music is used by people for music therapy and provision of mental support. Recently, in the field of music information science, studies on music classification have been conducted. However, most of these studies have classified music on the basis of melody and tempo. No classification method that is based on lyrics has been proposed for music therapy and the provision of mental support. Lyrics are an important element of music in addition to melody and tempo. Therefore we propose a music classification method based on lyrics. In this study, we conducted the emotion weighting of non-emotional words by using similarity in order to improve the previously proposed method that uses only emotional words. We realized emotion extraction from the entire lyrics by using non-emotional words in the proposed method. Further, we showed that a relatively high classification precision is obtained when compared with the conventional method.

References

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Mizuki Furuya, Hung-Hsuan Huang, Kyoji Kawagoe. Evaluation of Music Classification Method based on Lyrics of English Songs. International Multi-conference on Engineers and Computer Scientists. IMECS, pp 134--137, Hong Kong, March 18--20, 2015.
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Carlo Strapparava and Alessandro Valitutti. Wordnet-affect: an affective extension of wordnet. In Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon, 2004.
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Matt McVicar, Tim Freeman, and Tijl De. Bie. Mining the Correlation between Lyrical and Audio Features and the Emergence of Mood. 12th International Society for Music Information Retrieval Conference. ISMIR, pp783--788, Miami, Florida, USA, October 24--28, 2011.
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Laura-Lee Balkwill, William Forde Thompson. A cross-cultural investigation of the perception of emotion in music; Psychophysical and cultural cues. Music Perception, 17, 43--64, 1999.
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Cited By

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  • (2018)An HMM-Based Framework for Supporting Accurate Classification of Music DatasetsIntelligent Methods and Big Data in Industrial Applications10.1007/978-3-319-77604-0_20(277-289)Online publication date: 19-May-2018

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      cover image ACM Other conferences
      MoMM 2015: Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia
      December 2015
      422 pages
      ISBN:9781450334938
      DOI:10.1145/2837126
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • Johannes Kepler University, Linz, Austria
      • @WAS: International Organization of Information Integration and Web-based Applications and Services

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 11 December 2015

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      • (2018)An HMM-Based Framework for Supporting Accurate Classification of Music DatasetsIntelligent Methods and Big Data in Industrial Applications10.1007/978-3-319-77604-0_20(277-289)Online publication date: 19-May-2018

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