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- W2152156982 abstract "Very large online music databases have recently been created by vendors, but they generally lack content-based retrieval methods. One exception is Allmusic.com which offers browsing by musical emotion, using human experts to classify several thousand songs into 183 moods. In this paper, machine learning techniques are used instead of human experts to extract emotions in Music. The classification is based on a psychological model of emotion that is extended to 23 specific emotion categories. Our results for mining the lyrical text of songs for specific emotion are promising, generate classification models that are human-comprehensible, and generate results that correspond to commonsense intuitions about specific emotions. Mining lyrics focused in this paper is one aspect of research which combines different classifiers of musical emotion such as acoustics and lyrical text." @default.
- W2152156982 created "2016-06-24" @default.
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- W2152156982 date "2009-01-01" @default.
- W2152156982 modified "2023-10-02" @default.
- W2152156982 title "Music Emotion Identification from Lyrics" @default.
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- W2152156982 doi "https://doi.org/10.1109/ism.2009.123" @default.
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