English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23461432      Online Users : 718
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/124724
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124724


    Title: 針對情感商品的情境感知推薦系統-以流行音樂為例
    Context-Awareness Music Recommendation System – The Case of Taiwan Pop Music
    Authors: 林青峰
    Lin, Qing-Feng
    Contributors: 楊亨利
    Yang, Heng-Li
    林青峰
    Lin, Qing-Feng
    Keywords: 意見分析
    音樂推薦系統
    情境感知
    Opinion Mining
    Music Recommendation System
    Context-Awareness
    Date: 2019
    Issue Date: 2019-08-07 16:09:32 (UTC+8)
    Abstract: 情感商品,如音樂、電影、小說…等,與一般單純為了使用功能的功能商品有很大的不同。因為情感商品的評價與個人感受有關,情感商品在網路上通常會存在比較多見仁見智的評論;商品的效用也更與商品本身內容及通常能帶給使用者什麼感覺來的有關;而當下的使用情境適合那些情感商品更是情感商品與功能商品很不相同的地方。
    一般考慮到網路評論的推薦系統,通常只有單獨從網路評論裏分析與評論與商品屬性的關係,較少同時考慮商品其它實質內容像是音樂的歌詞、音律對商品評價的影響。而這類推薦系統主要在找正負傾向規則,也較少討論找出像是「聽了讓人感到很遺憾」這種引發人類情緒的情感商品效用規則。另外傳統的推薦系統通常也會忽略在不同的情境之下,人們的商品偏好會有很大差別的現象;像在運動時喜歡聽很動感音樂的人,並不一定在睡前的情境也會喜歡類似的音樂。在這樣的背景之下,如何建立一個能同時有效的從網路評論及音樂內容找出考慮到商品能有的心情效能並能感知情境的內容推薦系統是有其研發重要性的。
    本研究以流行音樂這個情感商品為例,提出一個雛型架構來達成以上的目標。首先本研究先建立能了解網路評論狀況的情感標籤分類器,用於隨時了解某商品目前網路評論的情感傾向;同時本研究也建立一個同時考慮到音樂歌詞及音質特性的音樂內容分類器,用於從音樂的內容特徵來得到某音樂商品可能音樂情感傾向。經過資料的收集、分析與訓練,整體的網路評論情緒傾向分類器經過測試平均的F1有70.09%的分類成功率;而音樂內容情感傾向分類器的巨觀平均分類成功率F1有74.89%、微觀平均分類成功率F1則是到達了80.13%。
    利用這二個分類器,加上本研究建立的運動及安眠情境感知雛型系統與偏好資料,本研究提出了符合情感商品特性的情境感知商品推薦系統。最後本研究設計了三個實驗來驗證情緒分類器及情境感知系統的有效性。從情緒音樂實驗中我們發現利用由本研究分類器所分類出的喜悅與平靜的音樂,可以有效的降低受測者的悲傷度並增加控制度;另外從實驗結果中可以得知音樂推薦的順序雖沒有明顯影響悲傷度及控制度的變化,但會影響收聽者的滿意度。從運動及安眠情境實驗的訪談資料中,我們可以得到一些對本研究的情境感知雛形系統的正面測試回饋結果。這對未來開發類似系統會有重要的幫助。
    Emotional products, such as music, movies, novels… etc., are quite different from functional products. Because the evaluation of emotional products is related to personal feelings, emotional products have much more kinds of user reviews on the Internet than functional products. People will more likely choose an emotional product because of the content and People will more likely use different emotional products then functional products in the different situations.
    In the past researches, an opionion mining based recommendation system usually only use web reviews to recommend products, there were not many studies use both user reviews and the content of the product at the same time. This kind of recommendation system was also only driven by positive or negative tendency rules, and there are also few discussions to find out the emotional rules, such like “this music is very happy to hear.” In addition, the traditional recommendation system usually ignores the fact that people's preference for emotional products will be very different in different contexts; people who like to listen to rock music during exercise are not necessarily like rock music at bedtime. Under such a background, how to establish a context-awareness recommendation system that can effectively and effectively help people to choose emotional products by using both online reviews and product content is very important.
    In this study, we took pop music as a case of emotional products and we had proposed a recommendation prototype system use both web reviews opinion mining and a lyrics and sound content-based tags classifier to be the recommendation sources. After data collection, analysis and training processes, the overall web reviews opinion classifier accuracy average F1 score is 70.09%; the music content emotional tags classifier accuracy marco-average F1 score is 74.89% and micro-average F1 score is 80.13%.
    Using these two classifiers, a context-aware prototype system was proposed in this study, we had completed a context-aware product recommendation system that meets the characteristics of emotional goods. Finally, we designed three experiments to verify the effectiveness of the emotional classifier and context-aware system.
    In the emotional music experiment, we found that by using joy and calm music content classifier trained in this study can effectively help to reduce the sadness and increase the degree of control. In addition, we can also found that the recomendtion order joy-clam and clam-joy was no significant different in emotion regulation, but it will affect satisfaction. From the interview data of the exercise and sleeping context experiments, we got some positive feedback results for the context-aware prototype system of this study. This will be some help in developing similar systems in the future.
    Reference: 1. 卓淑玲; 陳學志; 鄭昭明(2013),『台灣地區華人情緒與相關心理生理資料庫─ 中文情緒詞常模研究』,中華心理學刊,第五十五卷,第四期,頁493-523。
    2. 陳威佑(2012),『基於前進選擇之特徵選取之流行音樂曲風辨識與分析』。國立中山大學電機工程學系碩士論文,未出版,高雄。
    3. 楊亨利; 林青峰(2017),『微網誌短句的情感指數分析-以新浪微博為例』,資訊管理學報,第二十四卷,第一期,頁1-28。
    4. 楊亨利; 林青峰(2018),『應用網路評價的功能商品推薦系統』,資訊管理學報,第二十五卷,第三期,頁335-361。
    5. 盧毓文(2011),『情緒對類別學習之影響』。國立政治大學心理學研究所碩士論文,未出版,台北。
    6. Bergstra, J., Casagrande, N., Erhan, D., Eck, D., & Kegl, B. (2006). Aggregate features and ADABOOST for music classification. Machine learning, 65(2-3), pp. 473-484.
    7. Billsus, D., & Pazzani, M. J. (1998). Learning Collaborative Information Filters. In Icml 98, pp. 46-54.
    8. Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., Zhang, L., & He, X. (2010). Music recommendation by unified hypergraph: combining social media information and music content. In Proceedings of the 18th ACM international conference on Multimedia, pp. 391-400. ACM.
    9. Burger, B., Thompson, M. R., Luck, G., Saarikallio, S., & Toiviainen, P. (2013). Influences of rhythm-and timbre-related musical features on characteristics of music-induced movement. Frontiers in psychology, 4, 183. https://doi.org/10.3389/fpsyg.2013.00183
    10. Celma, Ò. (2006). Foafing the music: Bridging the semantic gap in music recommendation. In International Semantic Web Conference, pp. 927-934. Springer, Berlin, Heidelberg.
    11. Chaovalit, P., & Zhou, L. (2005), Movie review mining: A comparison between supervised and unsupervised classification approaches. In Proceeding of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05). Hawaii, USA.
    12. Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: the role of brand loyalty. Journal of marketing, 65(2), pp. 81-93.
    13. Chen, H. C., & Chen, A. L. (2001). A music recommendation system based on music data grouping and user interests. In Proceedings of the tenth international conference on Information and knowledge management, pp. 231-238. ACM.
    14. Cho, Y.H., & Kim, J.K. (2004), Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26(2), pp. 233-246.
    15. Corona, H., & O’Mahony, M. P. (2015). An exploration of mood classification in the million songs dataset. In 12th Sound and Music Computing Conference, Maynooth University, Ireland, 26 July-1 August 2015. Music Technology Research Group, Department of Computer Science Maynooth University.
    16. Dave, K., Lawrence, S., & Pennock, D.M. (2003), Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web, pp.519-528. Budapest, Hungary.
    17. De Oliveira, R., & Oliver, N. (2008). TripleBeat: enhancing exercise performance with persuasion. In Proceedings of the 10th international conference on Human computer interaction with mobile devices and services, pp. 255-264. ACM.
    18. Dornbush, S., English, J., Oates, T., Segall, Z., & Joshi, A. (2007). XPod: A human activity aware learning mobile music player. In Proceedings of the Workshop on Ambient Intelligence, 20th International Joint Conference on Artificial Intelligence (IJCAI-2007).
    19. Esuli, A., & Sebastiani, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. In LREC, 6, pp. 417-422.
    20. Goldberg, D., Nichols, D., Oki, B.M., & Terry, D. (1992), Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), pp. 61-70.
    21. Goudas, T., Louizos, C., Petasis, G., & Karkaletsis, V. (2015), Argument extraction from news, blogs, and the social web. International Journal on Artificial Intelligence Tools, 24(5), 1540024. https://doi.org/10.1142/S0218213015400242
    22. Gómez, L. M., & Cáceres, M. N. (2017, June). Applying data mining for sentiment analysis in music. In International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 198-205. Springer, Cham.
    23. Han, B. J., Rho, S., Jun, S., & Hwang, E. (2010). Music emotion classification and context-based music recommendation. Multimedia Tools and Applications, 47(3), pp. 433-460.
    24. Hariri, N., Mobasher, B., & Burke, R. (2012). Context-aware music recommendation based on latenttopic sequential patterns. In Proceedings of the sixth ACM conference on Recommender systems, pp. 131-138. ACM.
    25. Hu, X., Downie, J. S., West, K., & Ehmann, A. F. (2005). Mining Music Reviews: Promising Preliminary Results. In ISMIR, pp. 536-539.
    26. Jing, N., Jiang, T., Du, J., & Sugumaran, V. (2018), Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website. Electronic Commerce Research, 18(1), pp. 159–179.
    27. Jothilakshmi, S., & Kathiresan, N. (2012). Automatic music genre classification for indian music. In Proc. Int. Conf. Software Computer App.
    28. Kaminskas, M., & Ricci, F. (2011). Location-adapted music recommendation using tags. In International Conference on User Modeling, Adaptation, and Personalization, pp. 183-194. Springer, Berlin, Heidelberg.
    29. Kaur, C., & Kumar, R. (2017). Study and analysis of feature based automatic music genre classification using Gaussian mixture model. In 2017 International Conference on Inventive Computing and Informatics (ICICI), pp. 465-468. IEEE.
    30. Kempf, D.S. (1999), Attitude formation from product trial: Distinct roles of cognition and affect for hedonic and functional products. Psychology & Marketing, 16(1), pp. 35-50.
    31. Kohavi, R. (1995), A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, 14(2), pp. 1137-1145.
    32. Kontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013), Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications, 40(10), pp. 4065-4074.
    33. Kumar, V., & Minz, S. (2013). Mood classifiaction of lyrics using SentiWordNet. In 2013 International Conference on Computer Communication and Informatics, pp. 1-5. IEEE.
    34. Lee, S. K., Cho, Y. H., & Kim, S. H. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 180(11), pp. 2142-2155.
    35. Lee, J. S., & Lee, J. C. (2007). Context awareness by case-based reasoning in a music recommendation system. In International Symposium on Ubiquitious Computing Systems, pp. 45-58. Springer, Berlin, Heidelberg.
    36. Lee, C. H., Shih, J. L., Yu, K. M., & Lin, H. S. (2009). Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features. IEEE Transactions on Multimedia, 11(4), pp.670-682.
    37. Lehtiniemi, A. (2008). Evaluating SuperMusic: Streaming context-aware mobile music service. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (pp. 314-321). ACM.
    38. Li, H., & Lu, W. (2017), Learning latent sentiment scopes for entity-level sentiment analysis. AAAI, 2007, pp. 3482-3489.
    39. Li, N., & Wu, D.D. (2010), Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), pp. 354-368.
    40. Linden, G., Smith, B., & York, J. (2003), Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), pp. 76-80.
    41. Liu, H., Hu, J., & Rauterberg, M. (2009). Music playlist recommendation based on user heartbeat and music preference. In Proceedings of the International Conference of Computer Technology and Development, 2009(ICCTD'09). 1, pp. 545-549. IEEE.
    42. Liu, H., & Singh, P. (2004). Focusing on conceptnet’s natural language knowledge representation. In Proceedings of the 8th Intl Conf. on Knowledge-Based Intelligent Information and Engineering Syst.
    43. Mandel, M. I., & Ellis, D. P. (2005). Song-level features and support vector machines for music classification. In Proceedings of the 6th Int. Symposium on Music Information Retrieval, pp.594-599. Landon, UK.
    44. McDonald, R., Hannan, K., Neylon, T., Wells, M., & Reynar, J. (2007). Structured models for fine-to-coarse sentiment analysis. In Proceedings of the 45th annual meeting of the association of computational linguistics, pp. 432-439.
    45. McKinney, M., & Breebaart, J. (2003). Features for audio and music classification. In Proceedings of the ISMIR, pp. 151-158, 2003.
    46. Missen, M.M.S., Boughanem, M., & Cabanac, G. (2013), Opinion mining: reviewed from word to document level. Social Network Analysis and Mining, 3(1), pp. 107–125.
    47. Mostafa, M.M. (2013), More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), pp. 4241–4251.
    48. Moore, K. S. (2013). A systematic review on the neural effects of music on emotion regulation: implications for music therapy practice. Journal of music therapy, 50(3), pp. 198-242.
    49. Nadler, R. T., Rabi, R., & Minda, J. P. (2010). Better mood and better performance: Learning rule-described categories is enhanced by positive mood. Psychological Science, 21(12), pp. 1770-1776.
    50. Nanopoulos, A., Rafailidis, D., Symeonidis, P., & Manolopoulos, Y. (2010). Musicbox: Personalized music recommendation based on cubic analysis of social tags. IEEE Transactions on Audio, Speech, and Language Processing, 18(2), pp. 407-412.
    51. Oramas, S., Espinosa-Anke, L., & Lawlor, A. (2016). Exploring customer reviews for music genre classification and evolutionary studies. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York City, United States of America, 7-11 August 2016.
    52. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), pp. 1-135.
    53. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. In Proceeding of the ACL-02 Conference on Empirical Methods in Natural Language Processing, 10, pp.79-86 , Pennsylvania, USA
    54. Park, H. S., Yoo, J. O., & Cho, S. B. (2006). A context-aware music recommendation system using fuzzy bayesian networks with utility theory. In Proceeding of the International conference on Fuzzy systems and knowledge discovery, pp. 970-979. Springer, Berlin, Heidelberg.
    55. Patel, D., & Trivedi, K. (2017). Research of music classification based on mood recognition. International Education and Research Journal, 3(5).
    56. Resnick, P., & Varian, H.R. (1997). Recommender systems. Communications of the ACM, 40(3), pp. 56-58.
    57. Rho, S., Han, B. J., & Hwang, E. (2009). SVR-based music mood classification and context-based music recommendation. In Proceedings of the 17th ACM international conference on Multimedia, pp. 713-716. ACM.
    58. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pp. 285-295. ACM.
    59. Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253-260. ACM.
    60. Sharma, V., Agarwal, A., Dhir, R., & Sikka, G. (2016). Sentiments mining and classification of music lyrics using SentiWordNet. In 2016 Symposium on Colossal Data Analysis and Networking (CDAN), pp. 1-6. IEEE.
    61. Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), pp. 427–437.
    62. Su, J. H., Yeh, H. H., Philip, S. Y., & Tseng, V. S. (2010). Music recommendation using content and context information mining. IEEE Intelligent Systems, 25(1).
    63. Turney, P.D., & Littman, M.L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4), pp. 315–346.
    64. Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5), pp. 293-302.
    65. Van den Oord, A., Dieleman, S., & Schrauwen, B. (2013). Deep content-based music recommendation. In Advances in neural information processing systems, pp. 2643-2651.
    66. Van Goethem, A., & Sloboda, J. (2011). The functions of music for affect regulation. Musicae scientiae, 15(2), pp. 208-228.
    67. Wang, X., Rosenblum, D., & Wang, Y. (2012). Context-aware mobile music recommendation for daily activities. In Proceedings of the 20th ACM international conference on Multimedia, pp. 99-108. ACM.
    68. Wang, X., & Wang, Y. (2014). Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the 22nd ACM international conference on Multimedia, pp. 627-636. ACM.
    69. Wiebe, J., Wilson, T., & Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(2-3), pp. 165-210.
    70. Wijnalda, G., Pauws, S., Vignoli, F., & Stuckenschmidt, H. (2005). A personalized music system for motivation in sport performance. IEEE pervasive computing, 4(3), pp. 26-32.
    71. Xu, C., Maddage, N. C., & Shao, X. (2005). Automatic music classification and summarization. IEEE transactions on speech and audio processing, 13(3), pp. 441-450.
    72. Xu, C., Maddage, N. C., Shao, X., Cao, F., & Tian, Q. (2003). Musical genre classification using support vector machines. In Processing of the International Conference of Acoustics, Speech, and Signal (ICASSP'03), 5, pp. 429-432. IEEE.
    73. Yan, X., Wang, J., & Chau, M. (2015). Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers, 17(3), pp. 645–657.
    74. Ye, Q., Shi, W., & Li, Y. (2006). Sentiment classification for movie reviews in Chinese by improved semantic oriented approach. In the Proceeding of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), Hawaii, USA.
    75. Zhang, C., Zeng, D., Li, J., Wang, F.Y., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), pp. 2474–2487.
    76. Zhuang, L., Jing, F., & Zhu, X. Y. (2006). Movie review mining and summarization. In the Proceedings of the 15th ACM international conference on Information and knowledge management, pp. 43-50. ACM.
    Description: 博士
    國立政治大學
    資訊管理學系
    993565021
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0993565021
    Data Type: thesis
    DOI: 10.6814/NCCU201900471
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    502101.pdf3822KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback