A qualitative and quantitative study of color emotion using valence-arousal
Shangfei WANG, Rui DING
A qualitative and quantitative study of color emotion using valence-arousal
This paper describes qualitative and quantitative analysis of color emotion dimension expression using a standard device-independent colorimetric system. To collect color emotion data, 20 subjects are required to report their emotion responses, using a valence-arousal emotion model, to 52 color samples that are chosen from CIELAB Lch color spaces. Qualitative analysis, including analysis of variance (ANOVA), Pearson’s correlation and Spearman’s rank correlation, shows that significant differences exist between responses to achromatic and chromatic stimuli, but there are no significant differences between chromatic samples. There is a positive linear relationship between lightness/chroma and valence-arousal dimensions. Subsequently, several classic predictors are used to quantitatively predict emotion induced by color attributes. Furthermore, several explicit color emotion models are developed by using multiple linear regression with stepwise and pace regression. Experimental results show that chroma and lightness have stronger effects on emotions than hue, which is consistent with our qualitative results and other psychological studies. Arousal has greater predictive value than valence.
color emotion / valence and arousal / qualitative / quantitative
[1] |
Wei C Y, Dimitrova N, Chang S F. Color-mood analysis of films based on syntactic and psychological models. In: Proceedings of the 2004 IEEE International Conference on Multimedia and Expo. 2004, 831-834
|
[2] |
Papachristos E, Tselios N K, Avouris N M. Inferring relations between color and emotional dimensions of a web site using Bayesian networks. In: Proceedings of IFIP TC13 International Conference on Human- Computer Interaction. 2005, 1075-1078
|
[3] |
Coursaris C K, Sweirenga S J, Watrall E. An empirical investigation of color temperature and gender effects on web aesthetics. Journal of Usability Studies, 2008, 3(3): 103-117
|
[4] |
Tsai H C, Hung C Y, Hung F K. Computer aided product color design with artificial intelligence. Computer-Aided Design and Applications, 2007, 4(1-6): 557-564
|
[5] |
Hsiao S W, Chiu F Y, Hsu H Y. A computer-assisted colour selection system based on aesthetic measure for colour harmony and fuzzy logic theory. Color Research and Application, 2008, 33(5): 411-423
CrossRef
Google scholar
|
[6] |
Solli M, Lenz R. Color based bags-of-emotions. In: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns. 2009, 573-580
|
[7] |
Bresin R. What is the color of that music performance? In: Proceedings of the International Computer Music Conference. 2005, 367-370
|
[8] |
Küller R, Mikellides B, Janssens J. Color, arousal, and performancea comparison of three experiments. Color Research and Application, 2009, 34(2): 141-152
CrossRef
Google scholar
|
[9] |
Anter K F, Billger M. Colour research with architectural relevance: how can different approaches gain from each other? Color Research and Application, 2010, 35(2): 145-152
|
[10] |
Nakamura T, Sakolnakorn O P N, Hansuebsai A, Pungrassamee P, Sato T. Emotion induced from colour and its language expression. In: Proceedings of Interim Meeting of the International Color Association. 2004, 29-36
|
[11] |
Suk H J. Color and emotion—a study on the affective judgment across media and in relation to visual stimuli. Dissertation for the Doctoral Degree. Mannheim: University of Mannheim, 2006
|
[12] |
Valdez P, Mehrabian A. Effects of color on emotions. Journal of Experimental Psychology, 1994, 123(4): 394-409
|
[13] |
Xin J H, Cheng K M, Chong T F, Sato T, Nakamura T, Kajiwara K, Hoshino H. Quantifying colour emotional-what has been achieved. Research Journal of Textile and Apparel, 1998, 2(1): 46-54
|
[14] |
Xin J H, Cheng K M, Taylor G, Sato T, Hansuebsai A. Cross-regional comparison of colour emotions part I: quantitative analysis. Color Research and Application, 2004, 29(6): 451-457
CrossRef
Google scholar
|
[15] |
Xin J H, Cheng K M, Taylor G, Sato T, Hansuebsai A. Cross-regional comparison of colour emotions part II: qualitative analysis. Color Research and Application, 2004, 29(6): 458-466
CrossRef
Google scholar
|
[16] |
Ou L C, Luo M. R,Woodcock A,Wright A. A study of colour emotion and colour preference. Part I: colour emotions for single colours. Color Research and Application, 2004, 29(3): 232-240
CrossRef
Google scholar
|
[17] |
Ou L C, Luo M. R, Woodcock A, Wright A. A study of colour emotion and colour preference. Part III: colour preference modeling. Color Research and Application, 2004, 29(5): 381-389
CrossRef
Google scholar
|
[18] |
Gao X P, Xin J H, Sato T, Hansuebsai A, Scalzo M, Kajiwara K. Analysis of cross-cultural color emotion. Color Research and Application, 2007, 32(3): 223-229
CrossRef
Google scholar
|
[19] |
Gao X P, Xin H. Investigation of human’s emotional responses on colors. Color Research and Application, 2006, 31(5): 411-417
CrossRef
Google scholar
|
[20] |
Manav B. Color-emotion associations and color preferences: a case study for residences. Color Research and Application, 2007, 32(2): 144-150
CrossRef
Google scholar
|
[21] |
Kuo W G. The feasibility of establishing new color image scales using the magnitude estimation method. Color Research and Application, 2007, 32(6): 463-468
CrossRef
Google scholar
|
[22] |
Arapakis I, Arapakis I. Theories, methods and current research on emotions in library and information science, information retrieval and human-computer interaction. Information Processing & Management, 2011, 47(4): 575-592
CrossRef
Google scholar
|
[23] |
Lang P, Bradley M, Cuthbert B. International affective picture system (iaps): instruction manual and affective ratings. Technical Report A-6, The Center for Research in Psychophysiology, University of Florida, 2005
|
[24] |
Cohen J, Cohen P, West S G, Aiken L S. Applied multiple regression/ correlation analysis for the behavioral sciences. 3rd ed. Hillsdale: Lawrence Erlbaum, 2003
|
[25] |
Wang Y, Witten I H. Modeling for optimal probability prediction. In: Proceedings of the 19th International Conference on Machine Learning. 2002, 650-657
|
[26] |
Wang Y. A new approach to fitting linear models in high dimensional spaces. Dissertation for the Doctoral Degree. Hamilton: University of Waikato, 2000
|
[27] |
Shevade S K, Keerthi S S, Bhattacharyya C, Murthy K R K. Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks, 2000, 11(5): 1188-1193
CrossRef
Google scholar
|
[28] |
Smola A J, Sch ölkopf B. A tutorial on support vector regression. Technical report, Statistics and Computing, 2003
|
[29] |
Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed. Upper Saddle River: Prentice Hall, 1998
|
[30] |
Witten I H, Frank E, Hall MA. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed. San Francisco: Morgan Kaufmann, 2011
|
/
〈 | 〉 |