針對情感商品的推薦機制－以流行音樂為例

Recommended Mechanism for Hedonic Products-Taking Pop Music as an Example

楊亨利(Heng-Li Yang)；林青峰(Qing-Feng Lin)

情感分析 ； 流行音樂 ； 意見挖掘 ； 網路評論 ； 推薦規則 ； sentiment analysis ； pop music ； opinion mining ； internet review ； recommendation mechanism

資訊管理學報

27卷2期（2020 / 04 / 30）

175 - 204

繁體中文

情感商品，如音樂、電影等，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主觀的評論；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與情緒來的有關。傳統上，對於網路評論，我們通常只關注評論中所述及的商品屬性，主要在找正負傾向規則，而不會去企圖找出像是「聽了讓人感到很遺憾」這種引發人類情緒的情感商品規則。本研究以流行音樂這個情感商品為例，提出一個針對情感商品的推薦機制。首先我們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；另外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，網路評論分類器與音樂內容分類器的精準率、召回率與F1均達令人滿意程度，進而本研究以實驗分析在用戶悲傷情緒下應推薦的音樂來說明情感商品的推薦規則建立過程。

Purpose-This study aims to propose a mechanism based on web reviews opinion mining and product contents (e.g., audio and lyrics in our case) for hedonic product recommendation. Design/methodology/approach - The classifiers, web review SVM classifiers and music content SVM classifiers, were proposed and a prototype was also built. Finally, we designed an experiment for exemplifying the process of determining the recommended product when the user is in a particular mood. Findings-The acceptable precision, recall, F1 ratio were obtained for the two classifiers. The experiment indicated the recommendation rule while users are in sad mood. Research limitations/implications-We only take as an example of pop music. Other hedonic products (e.g., dancing) might be more complicated to analyze their contents owing to video. Practical implications-Following our proposed mechanism, the suppliers of hedonic products would know how to recommend proper contents to users to invoke their desirable feelings. Originality/value-The proposed mechanism is brand new. As we know, there is no such a recommended mechanism for hedonic product in literature.

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，55(4)，493-523。
   連結：
2. 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，55(4)，493-523。
   連結：
3. 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，55(4)，493-523。
   連結：
4. 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。中華民國資訊管理學報，24(1)，1-28。
   連結：
5. 楊亨利,林青峰(2018)。應用網路評價的功能商品推薦系統。中華民國資訊管理學報，25(3)，335-361。
   連結：
6. Bergstra, J.,Casagrande, N.,Erhan, D.,Eck, D.,Kegl, B.(2006).Aggregate features and ADABOOST for music classification.Machine Learning,65(2-3),473-484.
7. 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.Proceedings of the 18th ACM international conference on Multimedia,Firenze, Italy:
8. 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,4,1-10.
9. Celma, O.(2006).FOAFing the music: Bridging the semantic gap in music recommendation.Proceedings of the International Semantic Web Conference,Berlin, Heidelberg:
10. 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),81-93.
11. Chen, H.C.,Chen, A.L.(2001).A music recommendation system based on music data grouping and user interests.Proceedings of the 10th International Conference on Information and Knowledge Management,Atlanta, U.S.A.:
12. Corona, H.,O’Mahony, M.P.(2015).An exploration of mood classification in the million songs dataset.Proceedings of the 12th Sound and Music Computing Conference,Maynooth, Ireland:
13. Esuli, A.,Sebastiani, F.(2006).Sentiwordnet: A publicly available lexical resource for opinion mining.LREC,6,417-422.
14. Gómez, L.M.,Cáceres, M.N.(2017).Applying data mining for sentiment analysis in music.Proceedings of the 15th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2017),Porto, Portugal:
15. Hu, X.,Downie, J.S.(2010).Improving mood classification in music digital libraries by combining lyrics and audio.Proceedings of the 10th annual joint conference on Digital libraries,Surfer's Paradise, Australia:
16. Hu, X.,Downie, J.S.,West, K.,Ehmann, A.F.(2005).Mining music reviews: Promising preliminary results.Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005),London, United Kingdom:
17. Hu, X.,Yang, Y.H.(2017).The mood of Chinese pop music: Representation and recognition.Journal of the Association for Information Science and Technology,68(8),1899-1910.
18. Jothilakshmi, S.,Kathiresan, N.(2012).Automatic music genre classification for indian music.Proceedings of the International Conference on Software and Computer Applications (ICSCA 2012),Singapore:
19. Kaur, C.,Kumar, R.(2017).Study and analysis of feature based automatic music genre classification using Gaussian mixture model.Proceedings of the 2017 International Conference on Inventive Computing and Informatics (ICICI 2017),Coimbatore, India:
20. Kempf, D.S.(1999).Attitude formation from product trial: Distinct roles of cognition and affect for hedonic and functional products.Psychology & Marketing,16(1),35-50.
21. Kontopoulos, E.,Berberidis, C.,Dergiades, T.,Bassiliades, N.(2013).Ontologybased sentiment analysis of twitter posts.Expert Systems with Applications,40(10),4065-4074.
22. Kumar, V.,Minz, S.(2013).Mood classifiaction of lyrics using SentiWordNet.Proceedings of the 2013 International Conference on Computer Communication and Informatics (ICCCI 2013),Coimbatore, India:
23. 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),670-682.
24. Lee, S.K.,Cho, Y.H.,Kim, S.H.(2010).Collaborative filtering with ordinal scalebased implicit ratings for mobile music recommendations.Information Sciences,180(11),2142-2155.
25. Li, H.,Lu, W.(2017).Learning latent sentiment scopes for entity-level sentiment analysis.Proceedings of the 31st AAAI conference (AAAI-17),San Francisco, U.S.A.:
26. Li, N.,Wu, D.D.(2010).Using text mining and sentiment analysis for online forums hotspot detection and forecast.Decision Support Systems,48(2),354-368.
27. Liu, H.,Singh, P.(2004).Focusing on ConceptNet’s natural language knowledge representation.Proceedings of the 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES 2004),Wellington, New Zealand:
28. 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),198-242.
29. Mostafa, M.M.(2013).More than words: Social networks’ text mining for consumer brand sentiments.Expert Systems with Applications,40(10),4241-4251.
30. 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),1770-1776.
31. 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),407-412.
32. Oord, A.,Dieleman, S.,Schrauwen, B.(2013).Deep content-based music recommendation.Advances in Neural Information Processing Systems
33. Oramas, S.,Espinosa-Anke, L.,Lawlor, A.(2016).Exploring customer reviews for music genre classification and evolutionary studies.Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016),New York, U.S.A.:
34. Pang, B.,Lee, L.(2008).Opinion mining and sentiment analysis.Foundations and Trends in Information Retrieval,2(1-2),1-135.
35. Patel, D.,Trivedi, K.(2017).Research of music classification based on mood recognition.International Education and Research Journal,3(5),544-555.
36. Rho, S.,Han, B.J.,Hwang, E.(2009).SVR-based music mood classification and context-based music recommendation.Proceedings of the 17th ACM International Conference on Multimedia,Beijing, China:
37. Schein, A.I.,Popescul, A.,Ungar, L.H.,Pennock, D.M.(2002).Methods and metrics for cold-start recommendations.Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,Tampere, Finland:
38. Sharma, V.,Agarwal, A.,Dhir, R.,Sikka, G.(2016).Sentiments mining and classification of music lyrics using SentiWordNet.Proceedings of the 2016 Symposium on Colossal Data Analysis and Networking (CDAN 2016),Indore, India:
39. Sokolova, M.,Lapalme, G.(2009).A systematic analysis of performance measures for classification tasks.Information Processing and Management,45(4),427-437.
40. Turney, P.D.,Littman, M.L.(2003).Measuring praise and criticism: Inference of semantic orientation from association.ACM Transactions on Information Systems,21(4),315-346.
41. Tzanetakis, G.,Cook, P.(2002).Musical genre classification of audio signals.IEEE Transactions on Speech and Audio Processing,10(5),293-302.
42. Van Goethem, A.,Sloboda, J.(2011).The functions of music for affect regulation.Musicae Scientiae,15(2),208-228.
43. Wang, J.,Zhao, X.(2019).,未出版
44. Wang, X.,Wang, Y.(2014).Improving content-based and hybrid music recommendation using deep learning.Proceedings of the 22nd ACM International Conference on Multimedia,Orlando, U.S.A.:
45. Wiebe, J.,Wilson, T.,Cardie, C.(2005).Annotating expressions of opinions and emotions in language.Language Resources and Evaluation,39(2-3),165-210.
46. Xu, C.,Maddage, N.C.,Shao, X.(2005).Automatic music classification and summarization.IEEE Transactions on Speech and Audio Processing,13(3),441-450.
47. Xu, C.,Maddage, N.C.,Shao, X.,Cao, F.,Tian, Q.(2003).Musical genre classification using support vector machines.Proceedings of the International Conference of Acoustics, Speech, and Signal (ICASSP'03),Hong Kong, China:
48. Yan, X.,Wang, J.,Chau, M.(2015).Customer revisit intention to restaurants: Evidence from online reviews.Information Systems Frontiers,17(3),645-657.
49. Ye, Q.,Shi, W.,Li, Y.(2006).Sentiment classification for movie reviews in Chinese by improved semantic oriented approach.Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06),Koloa, U.S.A.:
50. Zhang, C.,Evangelopoulos, G.,Voinea, S.,Rosasco, L.,Poggio, T.(2014).A deep representation for invariance and music classification.Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),Florence, Italy:
51. Zhuang, L.,Jing, F.,Zhu, X.Y.(2006).Movie review mining and summarization.Proceedings of the 15th ACM International Conference on Information and Knowledge Management,Arlington, U.S.A.:
52. 陳威佑(2012)。高雄，國立中山大學電機工程學系研究所。
53. 盧毓文(2011)。台北，國立政治大學心理學研究所。