Browse > Article
http://dx.doi.org/10.17703/IJACT.2022.10.3.352

A Study on the Sentiment Analysis of Contemporary Pop Musicians and Classical Music Composers  

Park, Youngjoo (Dept. of Music Education, Kyungnam Univ.)
Publication Information
International Journal of Advanced Culture Technology / v.10, no.3, 2022 , pp. 352-359 More about this Journal
Abstract
The study examined a sentiment analysis based on Tweeter messages between contemporary pop musicians and classical music composers. Musicians of each genre were carefully selected for the sentiment analysis. Many opinion messages on Tweets that users have discussed were collected, and the messages were evaluated by using Naïve Bayes Classifier. The results demonstrated that users showed high positive sentiments for the two different genres. However, on average, the positive sentiment values for classical music composers are higher than for contemporary pop musicians. In addition, the rankings of the highest positive sentiments among contemporary pop musicians and classical music composers did not coincide with the popularity of the two different genres of musicians. This study will contribute to the study of future sentimental analysis between music and musicians.
Keywords
Sentiment Analysis; Twitter; Contemporary Pop Musicians; Classical Music Composers;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P. N. Juslin and D. Vastfjall, "Emotional responses to music: The need to consider underlying mechanisms," Behavioral and Brain Sciences, Vol. 31, No. 5, pp. 559-575, 2008.   DOI
2 A. E. Krause, A. C. North and L. Y. Hewitt, "Music-listening in everyday life: Devices and choice," Psychology of Music, Vol. 43, No. 2, pp. 155-170, 2015.   DOI
3 M. Daniel, R. Neves and N. Horta, "Company event popularity for financial markets using Twitter and sentiment analysis," Expert Systems with Applications, Vol. 71, pp. 111-124, 2017.   DOI
4 The Guardian https://www.theguardian.com/news/datablog/2013/apr/19/twitter-music-app-100-most-followed-musicians
5 L. A. Liikkanen, K. Jakubowski and J. M. Toivanen, "Catching earworms on Twitter: Using big data to study involuntary musical imagery," Music Perception: An Interdisciplinary Journal, Vol. 33, No. 2, pp. 199-216, 2015.   DOI
6 Discogs https://www.discogs.com/lists/The-50-Greatest-Composers/1571
7 New York Times https://www.nytimes.com/2011/01/23/arts/music/23composers.html
8 K. N. Olsen, M. Powell, A. Anic, R. J. Vallerand and W. F. Thompson, "Fans of violent music: The role of passion in positive and negative emotional experience," Musicae Scientiae, Vol. 26, No. 2, pp. 364-387, 2020.
9 N. Logeswaran and J. Bhattacharya, "Crossmodal transfer of emotion by music," Neuroscience Letters, Vol. 455, No. 2, pp. 129-133, 2009.   DOI
10 L. Lundqvist, F. C. Carlsson, P. Hilmersson and P. Juslin, "Emotional responses to music: Experience, expression, and physiology," Psychology of Music, Vol. 37, No. 1, pp. 61-90, 2009.   DOI
11 B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval, Vol. 2, No.1-2, pp. 1-135, 2008.   DOI
12 J. Read, "Using emoticons to reduce dependency in machine learning techniques for sentiment classification," in Proc. Association for Computing Linguistics Student Research Workshop, pp. 43-48, 2004.
13 C. Yang, K. H. Lin and H. H. Chen, "Emotion classification using web blog corpora," in Proc. 2007 IEEE/WIC/ACM International Conference: Web Intelligence, pp. 275-278, 2007.
14 S. Kim and F. Hovy, "Determining the sentiment of opinions," in Proc. 20th international conference: Computational Linguistics, pp. 1367-1373, 2004.
15 T. Hamling and A. Agrawal, "Sentiment analysis of Tweets to gain insights into the 2016 US election," Columbia Undergraduate Science Journal, Vol. 11, pp. 34-42, 2017.
16 K. Dave, S. Lawrence and D. Pennock, "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews," in Proc. 12th International Conference: Association for Computing Machinery, pp. 519-528, 2003.
17 A. Pak and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining," in Proc. 7 th International Conference: Language Resource and Evolution, pp. 1320-1326, 2010.
18 B. Pang, L. Lee and S. Vaithyanathan, "Thumbs up? Sentiment classification using machine learning techniques," in Proc. 2002 Conference: Empirical Methods in Natural Language Processing, pp. 79-86, 2002.