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http://dx.doi.org/10.5392/JKCA.2020.20.10.089

Development of the Artwork using Music Visualization based on Sentiment Analysis of Lyrics  

Kim, Hye-Ran (세종대학교 소프트웨어융합대학 만화애니메이션텍 전공)
Publication Information
The Journal of the Korea Contents Association / v.20, no.10, 2020 , pp. 89-99 More about this Journal
Abstract
In this study, we tried to produce moving-image works through sentiment analysis of music. First, Google natural language API was used for the sentiment analysis of lyrics, then the result was applied to the image visualization rules. In prior engineering researches, text-based sentiment analysis has been conducted to understand users' emotions and attitudes by analyzing users' comments and reviews in social media. In this study, the data was used as a material for the creation of artworks so that it could be used for aesthetic expressions. From the machine's point of view, emotions are substituted with numbers, so there is a limit to normalization and standardization. Therefore, we tried to overcome these limitations by linking the results of sentiment analysis of lyrics data with the rules of formative elements in visual arts. This study aims to transform existing traditional art works such as literature, music, painting, and dance to a new form of arts based on the viewpoint of the machine, while reflecting the current era in which artificial intelligence even attempts to create artworks that are advanced mental products of human beings. In addition, it is expected that it will be expanded to an educational platform that facilitates creative activities, psychological analysis, and communication for people with developmental disabilities who have difficulty expressing emotions.
Keywords
Sentiment Analysis; Natural Language Processing; Sound Visualization; Data Visualization; Art Creation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 손진훈, "정서의 심리적 모델 : 개별 정서 모델, 평가모델, 차원 모델을 중심으로," 대한인간공학회지, 제30권, 제1호, pp.179-186, 2011.
2 황민철, 김종화, 문성철, 박상인, "감성 모델링 및 인식 기술," 로봇과 인간, 제8권, 제4호, pp.34-44, 2011.
3 김동림, 림빈, 임영환, "감성 트리 기반의 음악 감성조절 알고리즘," 한국콘텐츠학회논문지, 제15권, 제3호, pp.21-31, 2015.   DOI
4 R. Thayer, The Origin of Everyday Moods: Managing Energy, Tension, and Stress, Oxford University Press, Oxford, 1996.
5 S. Hsiao, S. Chen, and C. Lee, "Methodology for stage lighting control based on music emotions," Information sciences, Vol.412, pp.14-35, 2017.   DOI
6 http://www.fractal.org/Bewustzijns-Besturings-Model/Nature-of-emotions.htm, 2020.7.24.
7 https://m.blog.naver.com/sehyunfa/221675809410, 2020.7.24.
8 https://cloud.google.com/natural-language/docs/basics?hl=ko, 2019.10.3.
9 https://ko.wikipedia.org/wiki/감성, 2020.7.6.
10 김혜란, 이상욱, "라반의 공간조화이론 '코레우틱스(Choreutics)'를 활용한 신체 움직임의 추상적 시각화 연구," 예술인문사회 융합 멀티미디어 논문지, Vol.7, No.3, pp.371-381, 2017.
11 배미영, 칸딘스키 조형이론과 작품분석을 통한 색채 특성에 관한 연구, 이화여자대학교, 석사학위논문, 2010.
12 X. Hu and J. Downie, "Improving mood classification in music digital libraries by combining lyrics and audio," In Proc. JCDL. ACM, pp.159-168, 2010.
13 H. Katayose and S, Inokuchi, "The Kansei music system," Comput. Music J, Vol.13, No.4, pp.72-77, 1989.   DOI
14 A. Joshi, P. Bhattacharyya, and M. J. Carman, "Automatic sarcasm detection: A surveym," ACM Computing Surveys (CSUR), Vol.50, No.5, pp.1-22, 2017.
15 I. Hernandez-Farías, J. Benedí, and P. Rosso, "Applying basic features from sentiment analysis for automatic irony detection," In Proceedings of the Conference on Pattern Recognition and Image Analysis, Springer, pp.337-344, 2015.
16 최수환, "음악 정보 추출(MIR) 알고리즘을 활용한 사 운드 시각화 연구," 에밀레, 제8권, pp.33-58, 2010.
17 이동섭, 김효영, 박진완, "L-system을 이용한 음악 시각화," 디지털디자인학연구, 제12권, 제2호, pp.67-77, 2012.
18 박진완, 김효영, "예술적 데이터 고찰," 디지털디자인학연구, 제11권, 제3호, pp.193-202, 2011.
19 L. Gomez and M. Caceres, "Applying data mining for sentiment analysis in music," PAAMS 2017, AISC, Springer, Cham, Vol.619, pp.198-205, 2017.
20 T. S. Utomo and R. Sarno, "Emotion Label from ANEW dataset for Searching Best Definition from WordNet," International Seminar on Application for Technology of Information and Communication (iSemantic), ACM, 2018.