DocumentCode :
2812494
Title :
Music Emotion Identification from Lyrics
Author :
Yang, Dan ; Lee, Won-Sook
Author_Institution :
Syst. Sci., Univ. of Ottawa, Ottawa, ON, Canada
fYear :
2009
fDate :
14-16 Dec. 2009
Firstpage :
624
Lastpage :
629
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.
Keywords :
content-based retrieval; data mining; emotion recognition; learning (artificial intelligence); music; content-based retrieval methods; lyrics mining; machine learning techniques; music emotion identification; online music databases; Content based retrieval; Data mining; Humans; Machine learning; Mood; Multimedia databases; Multimedia systems; Music information retrieval; Psychology; Text mining; Emotion; Lyrical Text; Music Information Retrieval; Text Classification; Text Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia, 2009. ISM '09. 11th IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-5231-6
Electronic_ISBN :
978-0-7695-3890-7
Type :
conf
DOI :
10.1109/ISM.2009.123
Filename :
5363083
Link To Document :
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