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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\n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\n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\n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\n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\n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\n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，\n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\n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\n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\n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\n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\n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\n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\nadaboost for music classification. Machine Learning, 65(2-3):473–484, 2006.\n[2] M. M. Bradley and P. J. Lang. Affective norms for english words (anew): Instruction\nmanual and affective ratings. Technical report, Technical Report C-1, The Center\nfor Research in Psychophysiology, University of Florida, 1999.\n[3] M. Brysbaert and B. New. Moving beyond kuˇcera and francis: A critical evaluation\nof current word frequency norms and the introduction of a new and improved word\nfrequency measure for american english. Behavior Research Methods, 41(4):977–\n990, 2009.\n[4] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM\nTransactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software\navailable at http://www.csie.ntu.edu.tw/˜cjlin/libsvm.\n[5] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–\n297, 1995.\n[6] A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for\nopinion mining. In Proceedings of the 5th Conference on Language Resources and\nEvaluation, pages 417–422, 2006.\n[7] Y. Feng, Y. Zhuang, and Y. Pan. Popular music retrieval by detecting mood. In\nProceedings of the 26th Annual International ACM SIGIR Conference on Research\nand Development in Informaion Retrieval, pages 375–376. ACM, 2003.\n[8] S. Hallam, I. Cross, and M. Thaut. Oxford handbook of music psychology. Oxford\nUniversity Press, 2008.\n[9] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd\nAnnual International ACM SIGIR Conference on Research and Development in Information\nRetrieval, pages 50–57. ACM, 1999.\n[10] X. Hu and J. S. Downie. Improving mood classification in music digital libraries by\ncombining lyrics and audio. In Proceedings of the 10th Annual Joint Conference on\nDigital Libraries, pages 159–168. ACM, 2010.\n[11] X. Hu and J. S. Downie. When lyrics outperform audio for music mood classification:\na feature analysis. In Proceedings of International Society of Music Information\nRetrieval Conference, pages 1–6, 2010.\n[12] X. Hu, J. S. Downie, and A. F. Ehmann. Lyric text mining in music mood classification.\nAmerican Music, 183(5,049):2–209, 2009.\n[13] Y. Hu, X. Chen, and D. Yang. Lyric-based song emotion detection with affective\nlexicon and fuzzy clustering method. In Proceedings of International Society of\nMusic Information Retrieval Conference, pages 123–128, 2009.\n[14] R. Kempter, V. Sintsova, C. Musat, and P. Pu. Emotionwatch: Visualizing finegrained\nemotions in event-related tweets. In Proceedings of the 8th International\nAAAI Conference on Weblogs and Social Media, 2014.\n[15] L.-W. Ku, Y.-T. Liang, and H.-H. Chen. Opinion extraction, summarization and\ntracking in news and blog corpora. In Proceedings of AAAI spring symposium:\nComputational approaches to analyzing weblogs, pages 100–107, 2006.\n[16] C. Laurier, J. Grivolla, and P. Herrera. Multimodal music mood classification using\naudio and lyrics. In Proceedings of the 7th International Conference on Machine\nLearning and Applications, pages 688–693. IEEE, 2008.\n[17] C. Laurier and P. Herrera. Audio music mood classification using support vector\nmachine.\n[18] J. H. Lee and J. S. Downie. Survey of music information needs, uses, and seeking\nbehaviours: Preliminary findings. In Proceedings of the 5th International Conference\non Music Information Retrieval, pages 441–446, 2004.\n[19] T. Li and M. Ogihara. Content-based music similarity search and emotion detection.\nIn Proceedings of IEEE International Conference on Acoustics, Speech, and Signal\nProcessing, volume 5, pages V–705. IEEE, 2004.\n[20] M. I. Mandel and D. P. Ellis. Song-level features and support vector machines for\nmusic classification. In Proceedings of International Conference on Music Information\nRetrieval, pages 594–599, 2005.\n[21] L. Martin and P. Pu. Prediction of helpful reviews using emotions extraction. In\nProceedings of the 28th AAAI Conference on Artificial Intelligence, 2014.\n[22] R. Mayer, R. Neumayer, and A. Rauber. Rhyme and style features for musical genre\nclassification by song lyrics. 2008.\n[23] M. F. Mckinney and J. Breebaart. Features for audio and music classification. In\nProceedings of International Conference on Music Information Retrieval, 2003.\n[24] R. Plutchik. The nature of emotions. American Scientist, 89:344, 2001.\n[25] J. F. Y. W. Robert J Ellis, Zhe Xing. Quantifying lexical novelty in song lyrics.\nIn Proceedings of the 16th International Society for Music Information Retrieval\nConference, 2015.\n[26] J. A. Russell. Affective space is bipolar. Journal of Personality and Social Psychology,\n37(3):345–356, 1979.\n[27] J. A. Russell. A circumplex model of affect. Journal of personality and social\npsychology, 39(6):1161–1178, 1980.\n[28] P. Saari and T. Eerola. Semantic computing of moods based on tags in social media\nof music. IEEE Transactions on Knowledge and Data Engineering, 26(10):2548–\n2560, 2014.\n[29] K. R. Scherer. What are emotions? and how can they be measured? Social Science\nInformation, 44(4):695–729, 2005.\n[30] V. Sintsova, C.-C. Musat, and P. Pu. Fine-grained emotion recognition in olympic\ntweets based on human computation. In Proceedings of the 4thWorkshop on Computational\nApproaches to Subjectivity, Sentiment and Social Media Analysis, number\nEPFL-CONF-197185, 2013.\n[31] P. J. Stone, D. C. Dunphy, and M. S. Smith. The general inquirer: A computer\napproach to content analysis. 1966.\n[32] G. Tzanetakis. Music analysis, retrieval and synthesis of audio signals marsyas. In\nProceedings of the 17th ACM International Conference on Multimedia, pages 931–\n932. ACM, 2009.\n[33] M. Van Zaanen and P. Kanters. Automatic mood classification using tf*idf based\non lyrics. In Proceedings of the 11th International Society of Music Information\nRetrieval Conference, pages 75–80, 2010.\n[34] Y.-H. Yang, Y.-C. Lin, H.-T. Cheng, I.-B. Liao, Y.-C. Ho, and H. H. Chen. Toward\nmulti-modal music emotion classification. In Proceedings of Pacific-Rim Conference\non Multimedia, pages 70–79. Springer, 2008.\n[35] Y.-H. Yang and J.-Y. Liu. Quantitative study of music listening behavior in a social\nand 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) https://ah.lib.nccu.edu.tw/item?item_id=123144-
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\n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\n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\n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\n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\n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\n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，\n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\n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\n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\n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\n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\n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\n的方法強化的。zh_TW
dc.description.tableofcontents 1 導論. . . . . . .1\n2 文獻探討. . . . . . .3\n2.1 情緒分類 . . . . . . .3\n2.2 音樂情緒辨識. . . . . . . . . . . 3\n2.2.1 聲音訊號. . . . . . . . . . 4\n2.2.2 後設資料（Metadata） . . 4\n2.2.3 歌詞文本. . . . . . . . . . 4\n2.3 自然語言處理中的情感辨識. . . 5\n2.4 歌詞的文字特性. . . . . . . . . . 5\n2.5 機器學習在分類問題上之應用. . 6\n3 研究方法. . . . . . .9\n3.1 Support Vector Machine . . . . . . 9\n3.1.1 實作. . . . . . . . . . . . 9\n3.1.2 參數選用. . . . . . . . . . 10\n3.2 特徵 . . . 10\n3.2.1 全文單字. . . . . . . . . . 10\n3.2.2 文本SUBTLEXus . . . . . 11\n3.2.3 情感單字. . . . . . . . . . 11\n3.3 資料集MER31k . . . . . . . . . . 11\n4 實驗設計與結果分析15\n4.1 實驗設定 15\n4.1.1 資料集. . . . . . . . . . . 15\n4.1.2 評估標準. . . . . . . . . . 16\n4.2 實驗結果與分析. . . . . . . . . . 16\n4.2.1 四象限的分類. . . . . . . 16\n4.2.2 象限對象限的分類. . . . 16\n5 結論. . . . . . .19\n5.1 結果討論 . . . . . . .19\n5.1.1 與過去研究之比較. . . . 19\n5.1.2 特徵分析. . . . . . . . . . 19\n5.2 未來發展方向. . . . . . . . . . . 20\n參考文獻. . . . . . .23zh_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\nadaboost for music classification. Machine Learning, 65(2-3):473–484, 2006.\n[2] M. M. Bradley and P. J. Lang. Affective norms for english words (anew): Instruction\nmanual and affective ratings. Technical report, Technical Report C-1, The Center\nfor Research in Psychophysiology, University of Florida, 1999.\n[3] M. Brysbaert and B. New. Moving beyond kuˇcera and francis: A critical evaluation\nof current word frequency norms and the introduction of a new and improved word\nfrequency measure for american english. Behavior Research Methods, 41(4):977–\n990, 2009.\n[4] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM\nTransactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software\navailable at http://www.csie.ntu.edu.tw/˜cjlin/libsvm.\n[5] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–\n297, 1995.\n[6] A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for\nopinion mining. In Proceedings of the 5th Conference on Language Resources and\nEvaluation, pages 417–422, 2006.\n[7] Y. Feng, Y. Zhuang, and Y. Pan. Popular music retrieval by detecting mood. In\nProceedings of the 26th Annual International ACM SIGIR Conference on Research\nand Development in Informaion Retrieval, pages 375–376. ACM, 2003.\n[8] S. Hallam, I. Cross, and M. Thaut. Oxford handbook of music psychology. Oxford\nUniversity Press, 2008.\n[9] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd\nAnnual International ACM SIGIR Conference on Research and Development in Information\nRetrieval, pages 50–57. ACM, 1999.\n[10] X. Hu and J. S. Downie. Improving mood classification in music digital libraries by\ncombining lyrics and audio. In Proceedings of the 10th Annual Joint Conference on\nDigital Libraries, pages 159–168. ACM, 2010.\n[11] X. Hu and J. S. Downie. When lyrics outperform audio for music mood classification:\na feature analysis. In Proceedings of International Society of Music Information\nRetrieval Conference, pages 1–6, 2010.\n[12] X. Hu, J. S. Downie, and A. F. Ehmann. Lyric text mining in music mood classification.\nAmerican Music, 183(5,049):2–209, 2009.\n[13] Y. Hu, X. Chen, and D. Yang. Lyric-based song emotion detection with affective\nlexicon and fuzzy clustering method. In Proceedings of International Society of\nMusic Information Retrieval Conference, pages 123–128, 2009.\n[14] R. Kempter, V. Sintsova, C. Musat, and P. Pu. Emotionwatch: Visualizing finegrained\nemotions in event-related tweets. In Proceedings of the 8th International\nAAAI Conference on Weblogs and Social Media, 2014.\n[15] L.-W. Ku, Y.-T. Liang, and H.-H. Chen. Opinion extraction, summarization and\ntracking in news and blog corpora. In Proceedings of AAAI spring symposium:\nComputational approaches to analyzing weblogs, pages 100–107, 2006.\n[16] C. Laurier, J. Grivolla, and P. Herrera. Multimodal music mood classification using\naudio and lyrics. In Proceedings of the 7th International Conference on Machine\nLearning and Applications, pages 688–693. IEEE, 2008.\n[17] C. Laurier and P. Herrera. Audio music mood classification using support vector\nmachine.\n[18] J. H. Lee and J. S. Downie. Survey of music information needs, uses, and seeking\nbehaviours: Preliminary findings. In Proceedings of the 5th International Conference\non Music Information Retrieval, pages 441–446, 2004.\n[19] T. Li and M. Ogihara. Content-based music similarity search and emotion detection.\nIn Proceedings of IEEE International Conference on Acoustics, Speech, and Signal\nProcessing, volume 5, pages V–705. IEEE, 2004.\n[20] M. I. Mandel and D. P. Ellis. Song-level features and support vector machines for\nmusic classification. In Proceedings of International Conference on Music Information\nRetrieval, pages 594–599, 2005.\n[21] L. Martin and P. Pu. Prediction of helpful reviews using emotions extraction. In\nProceedings of the 28th AAAI Conference on Artificial Intelligence, 2014.\n[22] R. Mayer, R. Neumayer, and A. Rauber. Rhyme and style features for musical genre\nclassification by song lyrics. 2008.\n[23] M. F. Mckinney and J. Breebaart. Features for audio and music classification. In\nProceedings of International Conference on Music Information Retrieval, 2003.\n[24] R. Plutchik. The nature of emotions. American Scientist, 89:344, 2001.\n[25] J. F. Y. W. Robert J Ellis, Zhe Xing. Quantifying lexical novelty in song lyrics.\nIn Proceedings of the 16th International Society for Music Information Retrieval\nConference, 2015.\n[26] J. A. Russell. Affective space is bipolar. Journal of Personality and Social Psychology,\n37(3):345–356, 1979.\n[27] J. A. Russell. A circumplex model of affect. Journal of personality and social\npsychology, 39(6):1161–1178, 1980.\n[28] P. Saari and T. Eerola. Semantic computing of moods based on tags in social media\nof music. IEEE Transactions on Knowledge and Data Engineering, 26(10):2548–\n2560, 2014.\n[29] K. R. Scherer. What are emotions? and how can they be measured? Social Science\nInformation, 44(4):695–729, 2005.\n[30] V. Sintsova, C.-C. Musat, and P. Pu. Fine-grained emotion recognition in olympic\ntweets based on human computation. In Proceedings of the 4thWorkshop on Computational\nApproaches to Subjectivity, Sentiment and Social Media Analysis, number\nEPFL-CONF-197185, 2013.\n[31] P. J. Stone, D. C. Dunphy, and M. S. Smith. The general inquirer: A computer\napproach to content analysis. 1966.\n[32] G. Tzanetakis. Music analysis, retrieval and synthesis of audio signals marsyas. In\nProceedings of the 17th ACM International Conference on Multimedia, pages 931–\n932. ACM, 2009.\n[33] M. Van Zaanen and P. Kanters. Automatic mood classification using tf*idf based\non lyrics. In Proceedings of the 11th International Society of Music Information\nRetrieval Conference, pages 75–80, 2010.\n[34] Y.-H. Yang, Y.-C. Lin, H.-T. Cheng, I.-B. Liao, Y.-C. Ho, and H. H. Chen. Toward\nmulti-modal music emotion classification. In Proceedings of Pacific-Rim Conference\non Multimedia, pages 70–79. Springer, 2008.\n[35] Y.-H. Yang and J.-Y. Liu. Quantitative study of music listening behavior in a social\nand affective context. IEEE Transactions on Multimedia, 15(6):1304–1315, 2013.zh_TW