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題名 基於歌詞文本分析技術探討音樂情緒辨識之方法研究
Exploring Music Emotion Recognition via Textual Analysis on Song Lyrics
作者 陳禔多
貢獻者 蔡銘峰
陳禔多
關鍵詞 音樂情緒辨識
日期 2017
上傳時間 1-Mar-2017 17:14:04 (UTC+8)
摘要 音樂是一種情感豐富的媒體。即使跨越了數個世紀，人們還是會
對同一首歌曲的情緒表達有類似的理解。然而在現今的數位音樂資料
庫可以看出，我們是不可能憑著人力完成數量如此龐大的音樂情緒辨
識，也因此期待電腦可以協助完成如此繁重的工作。隨著機器學習的
發展，電腦逐漸可以透過統計模型與數學模型判斷與辨識一些並未事
先提供規則的資料，而無法言傳的音樂情緒也得以有機會交由電腦辨
識、分類。雖然目前有許多透過訊號處理技術進行的音樂辨識研究，
但是透過歌詞文本的辨識卻是相對少見，使用的特徵也多侷限於通用
的文字資訊。本研究以音訊特徵為基礎，從不同的歌詞文本資訊出
發，透過分析歌詞文本進行歌曲情緒辨識，提供更多優化的參考資
訊，藉以提升歌曲於交流、表達、推薦等互動的功能性與準確性。實
驗結果發現，歌詞文本資訊對於歌曲的正負面情緒辨識確實有相當好
的表現，而對於特定分類的限制則是值得更多透過不同自然語言處理
的方法強化的。
參考文獻 [1] J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. K´egl. Aggregate features and
adaboost for music classification. Machine Learning, 65(2-3):473–484, 2006.
[2] M. M. Bradley and P. J. Lang. Affective norms for english words (anew): Instruction
manual and affective ratings. Technical report, Technical Report C-1, The Center
for Research in Psychophysiology, University of Florida, 1999.
[3] M. Brysbaert and B. New. Moving beyond kuˇcera and francis: A critical evaluation
of current word frequency norms and the introduction of a new and improved word
frequency measure for american english. Behavior Research Methods, 41(4):977–
990, 2009.
[4] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM
Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software
available at http://www.csie.ntu.edu.tw/˜cjlin/libsvm.
[5] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–
297, 1995.
[6] A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for
opinion mining. In Proceedings of the 5th Conference on Language Resources and
Evaluation, pages 417–422, 2006.
[7] Y. Feng, Y. Zhuang, and Y. Pan. Popular music retrieval by detecting mood. In
Proceedings of the 26th Annual International ACM SIGIR Conference on Research
and Development in Informaion Retrieval, pages 375–376. ACM, 2003.
[8] S. Hallam, I. Cross, and M. Thaut. Oxford handbook of music psychology. Oxford
University Press, 2008.
[9] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd
Annual International ACM SIGIR Conference on Research and Development in Information
Retrieval, pages 50–57. ACM, 1999.
[10] X. Hu and J. S. Downie. Improving mood classification in music digital libraries by
combining lyrics and audio. In Proceedings of the 10th Annual Joint Conference on
Digital Libraries, pages 159–168. ACM, 2010.
[11] X. Hu and J. S. Downie. When lyrics outperform audio for music mood classification:
a feature analysis. In Proceedings of International Society of Music Information
Retrieval Conference, pages 1–6, 2010.
[12] X. Hu, J. S. Downie, and A. F. Ehmann. Lyric text mining in music mood classification.
American Music, 183(5,049):2–209, 2009.
[13] Y. Hu, X. Chen, and D. Yang. Lyric-based song emotion detection with affective
lexicon and fuzzy clustering method. In Proceedings of International Society of
Music Information Retrieval Conference, pages 123–128, 2009.
[14] R. Kempter, V. Sintsova, C. Musat, and P. Pu. Emotionwatch: Visualizing finegrained
emotions in event-related tweets. In Proceedings of the 8th International
AAAI Conference on Weblogs and Social Media, 2014.
[15] L.-W. Ku, Y.-T. Liang, and H.-H. Chen. Opinion extraction, summarization and
tracking in news and blog corpora. In Proceedings of AAAI spring symposium:
Computational approaches to analyzing weblogs, pages 100–107, 2006.
[16] C. Laurier, J. Grivolla, and P. Herrera. Multimodal music mood classification using
audio and lyrics. In Proceedings of the 7th International Conference on Machine
Learning and Applications, pages 688–693. IEEE, 2008.
[17] C. Laurier and P. Herrera. Audio music mood classification using support vector
machine.
[18] J. H. Lee and J. S. Downie. Survey of music information needs, uses, and seeking
behaviours: Preliminary findings. In Proceedings of the 5th International Conference
on Music Information Retrieval, pages 441–446, 2004.
[19] T. Li and M. Ogihara. Content-based music similarity search and emotion detection.
In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal
Processing, volume 5, pages V–705. IEEE, 2004.
[20] M. I. Mandel and D. P. Ellis. Song-level features and support vector machines for
music classification. In Proceedings of International Conference on Music Information
Retrieval, pages 594–599, 2005.
[21] L. Martin and P. Pu. Prediction of helpful reviews using emotions extraction. In
Proceedings of the 28th AAAI Conference on Artificial Intelligence, 2014.
[22] R. Mayer, R. Neumayer, and A. Rauber. Rhyme and style features for musical genre
classification by song lyrics. 2008.
[23] M. F. Mckinney and J. Breebaart. Features for audio and music classification. In
Proceedings of International Conference on Music Information Retrieval, 2003.
[24] R. Plutchik. The nature of emotions. American Scientist, 89:344, 2001.
[25] J. F. Y. W. Robert J Ellis, Zhe Xing. Quantifying lexical novelty in song lyrics.
In Proceedings of the 16th International Society for Music Information Retrieval
Conference, 2015.
[26] J. A. Russell. Affective space is bipolar. Journal of Personality and Social Psychology,
37(3):345–356, 1979.
[27] J. A. Russell. A circumplex model of affect. Journal of personality and social
psychology, 39(6):1161–1178, 1980.
[28] P. Saari and T. Eerola. Semantic computing of moods based on tags in social media
of music. IEEE Transactions on Knowledge and Data Engineering, 26(10):2548–
2560, 2014.
[29] K. R. Scherer. What are emotions? and how can they be measured? Social Science
Information, 44(4):695–729, 2005.
[30] V. Sintsova, C.-C. Musat, and P. Pu. Fine-grained emotion recognition in olympic
tweets based on human computation. In Proceedings of the 4thWorkshop on Computational
Approaches to Subjectivity, Sentiment and Social Media Analysis, number
EPFL-CONF-197185, 2013.
[31] P. J. Stone, D. C. Dunphy, and M. S. Smith. The general inquirer: A computer
approach to content analysis. 1966.
[32] G. Tzanetakis. Music analysis, retrieval and synthesis of audio signals marsyas. In
Proceedings of the 17th ACM International Conference on Multimedia, pages 931–
932. ACM, 2009.
[33] M. Van Zaanen and P. Kanters. Automatic mood classification using tf*idf based
on lyrics. In Proceedings of the 11th International Society of Music Information
Retrieval Conference, pages 75–80, 2010.
[34] Y.-H. Yang, Y.-C. Lin, H.-T. Cheng, I.-B. Liao, Y.-C. Ho, and H. H. Chen. Toward
multi-modal music emotion classification. In Proceedings of Pacific-Rim Conference
on Multimedia, pages 70–79. Springer, 2008.
[35] Y.-H. Yang and J.-Y. Liu. Quantitative study of music listening behavior in a social
and affective context. IEEE Transactions on Multimedia, 15(6):1304–1315, 2013.
描述 碩士
國立政治大學
資訊科學學系
101753006
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1017530061
資料類型 thesis
dc.contributor.advisor 蔡銘峰zh_TW
dc.contributor.author (Authors) 陳禔多zh_TW
dc.creator (作者) 陳禔多zh_TW
dc.date (日期) 2017en_US
dc.date.accessioned 1-Mar-2017 17:14:04 (UTC+8)-
dc.date.available 1-Mar-2017 17:14:04 (UTC+8)-
dc.date.issued (上傳時間) 1-Mar-2017 17:14:04 (UTC+8)-
dc.identifier (Other Identifiers) G1017530061en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/106881-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 101753006zh_TW
dc.description.abstract (摘要) 音樂是一種情感豐富的媒體。即使跨越了數個世紀，人們還是會
對同一首歌曲的情緒表達有類似的理解。然而在現今的數位音樂資料
庫可以看出，我們是不可能憑著人力完成數量如此龐大的音樂情緒辨
識，也因此期待電腦可以協助完成如此繁重的工作。隨著機器學習的
發展，電腦逐漸可以透過統計模型與數學模型判斷與辨識一些並未事
先提供規則的資料，而無法言傳的音樂情緒也得以有機會交由電腦辨
識、分類。雖然目前有許多透過訊號處理技術進行的音樂辨識研究，
但是透過歌詞文本的辨識卻是相對少見，使用的特徵也多侷限於通用
的文字資訊。本研究以音訊特徵為基礎，從不同的歌詞文本資訊出
發，透過分析歌詞文本進行歌曲情緒辨識，提供更多優化的參考資
訊，藉以提升歌曲於交流、表達、推薦等互動的功能性與準確性。實
驗結果發現，歌詞文本資訊對於歌曲的正負面情緒辨識確實有相當好
的表現，而對於特定分類的限制則是值得更多透過不同自然語言處理
的方法強化的。		zh_TW
dc.description.tableofcontents 1 導論. . . . . . .1
2 文獻探討. . . . . . .3
2.1 情緒分類 . . . . . . .3
2.2 音樂情緒辨識. . . . . . . . . . . 3
2.2.1 聲音訊號. . . . . . . . . . 4
2.2.2 後設資料（Metadata） . . 4
2.2.3 歌詞文本. . . . . . . . . . 4
2.3 自然語言處理中的情感辨識. . . 5
2.4 歌詞的文字特性. . . . . . . . . . 5
2.5 機器學習在分類問題上之應用. . 6
3 研究方法. . . . . . .9
3.1 Support Vector Machine . . . . . . 9
3.1.1 實作. . . . . . . . . . . . 9
3.1.2 參數選用. . . . . . . . . . 10
3.2 特徵 . . . 10
3.2.1 全文單字. . . . . . . . . . 10
3.2.2 文本SUBTLEXus . . . . . 11
3.2.3 情感單字. . . . . . . . . . 11
3.3 資料集MER31k . . . . . . . . . . 11
4 實驗設計與結果分析15
4.1 實驗設定 15
4.1.1 資料集. . . . . . . . . . . 15
4.1.2 評估標準. . . . . . . . . . 16
4.2 實驗結果與分析. . . . . . . . . . 16
4.2.1 四象限的分類. . . . . . . 16
4.2.2 象限對象限的分類. . . . 16
5 結論. . . . . . .19
5.1 結果討論 . . . . . . .19
5.1.1 與過去研究之比較. . . . 19
5.1.2 特徵分析. . . . . . . . . . 19
5.2 未來發展方向. . . . . . . . . . . 20
參考文獻. . . . . . .23		zh_TW
dc.format.extent 1071564 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1017530061en_US
dc.subject (關鍵詞) 音樂情緒辨識zh_TW
dc.title (題名) 基於歌詞文本分析技術探討音樂情緒辨識之方法研究zh_TW
dc.title (題名) Exploring Music Emotion Recognition via Textual Analysis on Song Lyricsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. K´egl. Aggregate features and
adaboost for music classification. Machine Learning, 65(2-3):473–484, 2006.
[2] M. M. Bradley and P. J. Lang. Affective norms for english words (anew): Instruction
manual and affective ratings. Technical report, Technical Report C-1, The Center
for Research in Psychophysiology, University of Florida, 1999.
[3] M. Brysbaert and B. New. Moving beyond kuˇcera and francis: A critical evaluation
of current word frequency norms and the introduction of a new and improved word
frequency measure for american english. Behavior Research Methods, 41(4):977–
990, 2009.
[4] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM
Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software
available at http://www.csie.ntu.edu.tw/˜cjlin/libsvm.
[5] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–
297, 1995.
[6] A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for
opinion mining. In Proceedings of the 5th Conference on Language Resources and
Evaluation, pages 417–422, 2006.
[7] Y. Feng, Y. Zhuang, and Y. Pan. Popular music retrieval by detecting mood. In
Proceedings of the 26th Annual International ACM SIGIR Conference on Research
and Development in Informaion Retrieval, pages 375–376. ACM, 2003.
[8] S. Hallam, I. Cross, and M. Thaut. Oxford handbook of music psychology. Oxford
University Press, 2008.
[9] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd
Annual International ACM SIGIR Conference on Research and Development in Information
Retrieval, pages 50–57. ACM, 1999.
[10] X. Hu and J. S. Downie. Improving mood classification in music digital libraries by
combining lyrics and audio. In Proceedings of the 10th Annual Joint Conference on
Digital Libraries, pages 159–168. ACM, 2010.
[11] X. Hu and J. S. Downie. When lyrics outperform audio for music mood classification:
a feature analysis. In Proceedings of International Society of Music Information
Retrieval Conference, pages 1–6, 2010.
[12] X. Hu, J. S. Downie, and A. F. Ehmann. Lyric text mining in music mood classification.
American Music, 183(5,049):2–209, 2009.
[13] Y. Hu, X. Chen, and D. Yang. Lyric-based song emotion detection with affective
lexicon and fuzzy clustering method. In Proceedings of International Society of
Music Information Retrieval Conference, pages 123–128, 2009.
[14] R. Kempter, V. Sintsova, C. Musat, and P. Pu. Emotionwatch: Visualizing finegrained
emotions in event-related tweets. In Proceedings of the 8th International
AAAI Conference on Weblogs and Social Media, 2014.
[15] L.-W. Ku, Y.-T. Liang, and H.-H. Chen. Opinion extraction, summarization and
tracking in news and blog corpora. In Proceedings of AAAI spring symposium:
Computational approaches to analyzing weblogs, pages 100–107, 2006.
[16] C. Laurier, J. Grivolla, and P. Herrera. Multimodal music mood classification using
audio and lyrics. In Proceedings of the 7th International Conference on Machine
Learning and Applications, pages 688–693. IEEE, 2008.
[17] C. Laurier and P. Herrera. Audio music mood classification using support vector
machine.
[18] J. H. Lee and J. S. Downie. Survey of music information needs, uses, and seeking
behaviours: Preliminary findings. In Proceedings of the 5th International Conference
on Music Information Retrieval, pages 441–446, 2004.
[19] T. Li and M. Ogihara. Content-based music similarity search and emotion detection.
In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal
Processing, volume 5, pages V–705. IEEE, 2004.
[20] M. I. Mandel and D. P. Ellis. Song-level features and support vector machines for
music classification. In Proceedings of International Conference on Music Information
Retrieval, pages 594–599, 2005.
[21] L. Martin and P. Pu. Prediction of helpful reviews using emotions extraction. In
Proceedings of the 28th AAAI Conference on Artificial Intelligence, 2014.
[22] R. Mayer, R. Neumayer, and A. Rauber. Rhyme and style features for musical genre
classification by song lyrics. 2008.
[23] M. F. Mckinney and J. Breebaart. Features for audio and music classification. In
Proceedings of International Conference on Music Information Retrieval, 2003.
[24] R. Plutchik. The nature of emotions. American Scientist, 89:344, 2001.
[25] J. F. Y. W. Robert J Ellis, Zhe Xing. Quantifying lexical novelty in song lyrics.
In Proceedings of the 16th International Society for Music Information Retrieval
Conference, 2015.
[26] J. A. Russell. Affective space is bipolar. Journal of Personality and Social Psychology,
37(3):345–356, 1979.
[27] J. A. Russell. A circumplex model of affect. Journal of personality and social
psychology, 39(6):1161–1178, 1980.
[28] P. Saari and T. Eerola. Semantic computing of moods based on tags in social media
of music. IEEE Transactions on Knowledge and Data Engineering, 26(10):2548–
2560, 2014.
[29] K. R. Scherer. What are emotions? and how can they be measured? Social Science
Information, 44(4):695–729, 2005.
[30] V. Sintsova, C.-C. Musat, and P. Pu. Fine-grained emotion recognition in olympic
tweets based on human computation. In Proceedings of the 4thWorkshop on Computational
Approaches to Subjectivity, Sentiment and Social Media Analysis, number
EPFL-CONF-197185, 2013.
[31] P. J. Stone, D. C. Dunphy, and M. S. Smith. The general inquirer: A computer
approach to content analysis. 1966.
[32] G. Tzanetakis. Music analysis, retrieval and synthesis of audio signals marsyas. In
Proceedings of the 17th ACM International Conference on Multimedia, pages 931–
932. ACM, 2009.
[33] M. Van Zaanen and P. Kanters. Automatic mood classification using tf*idf based
on lyrics. In Proceedings of the 11th International Society of Music Information
Retrieval Conference, pages 75–80, 2010.
[34] Y.-H. Yang, Y.-C. Lin, H.-T. Cheng, I.-B. Liao, Y.-C. Ho, and H. H. Chen. Toward
multi-modal music emotion classification. In Proceedings of Pacific-Rim Conference
on Multimedia, pages 70–79. Springer, 2008.
[35] Y.-H. Yang and J.-Y. Liu. Quantitative study of music listening behavior in a social
and affective context. IEEE Transactions on Multimedia, 15(6):1304–1315, 2013.		zh_TW