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Volumn , Issue , 2010, Pages 159-168

Improving mood classification in music digital libraries by combining lyrics and audio

Author keywords

Experimentation; Measurement; Performance

Indexed keywords

ACCESS POINTS; CLASSIFICATION ACCURACY; DATA SETS; EXPERIMENTATION; FUSION METHODS; LEARNING CURVES; MUSIC DIGITAL LIBRARIES; ONLINE MUSIC; SENTIMENT ANALYSIS; TEXT FEATURE; TRAINING SAMPLE;

EID: 77955115533     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1816123.1816146     Document Type: Conference Paper
Times cited : (89)

References (31)
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    • Bradley, M.M.1    Lang, P.J.2
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    • (2008) Acoustical Science and Technology , vol.29 , Issue.4 , pp. 247-255
    • Downie, J.S.1
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    • Hu, X. 2010. Music and mood: where theory and reality meet. In Proceedings of iConference 2010.
    • (2010) Proceedings of IConference 2010
    • Hu, X.1
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    • Semi-supervised learning from different information sources
    • Li, T. and Ogihara, M. 2004. Semi-supervised learning from different information sources. Knowledge and Information Systems, 7 (3): 289-309.
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    • Li, T.1    Ogihara, M.2
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    • Affect analysis of text using fuzzy semantic typing
    • Subasic, P. and Huettner, A. 2001. Affect analysis of text using fuzzy semantic typing. IEEE Transactions on Fuzzy Systems, Special Issue, 9: 483-496.
    • (2001) IEEE Transactions on Fuzzy Systems , vol.9 , Issue.SPEC. ISSUE , pp. 483-496
    • Subasic, P.1    Huettner, A.2
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    • An evaluation of text classification methods for literary study
    • Yu, B. 2008. An evaluation of text classification methods for literary study, Literary and Linguistic Computing, 23(3): 327-343
    • (2008) Literary and Linguistic Computing , vol.23 , Issue.3 , pp. 327-343
    • Yu, B.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.