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Data mining in emotion color with affective computing

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Abstract

This research applies an innovative way to measure and identify user’s emotion with different ingredient color. How to find an intuitive way to understand human emotion is the key point in this research. The RGB color system that is widely used of all forms computer system is an accumulative color system in which red, green, and blue light are added together showing entire color. This study was based on Thayer’s emotion model which classifies the emotions with two vectors, valence and arousal, and gathers the emotion color with RGB as input for calculating and forecasting user’s emotion. In this experiment, using 320 data divide to quarter into emotion groups to train the weight in the neural network and uses 160 data to prove the accuracy. The result reveals that this model can be valid reckon the emotion by reply color response from user. In other hand, this experiment found that trend of the different ingredient of color on Cartesian coordinate system figures out the distinguishing intensity in RGB color system. Via the foregoing detect emotion model is going to design an affective computing intelligence framework try to embed the emotion component in it.

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Authors and Affiliations

  1. Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education Taichung, Taichung, Taiwan

    Min-Feng Lee & Guey-Shya Chen

  2. Department of Information Technology, Overseas Chinese University Taichung, Taichung, Taiwan

    Jason C. Hung

  3. Department of Management Information Systems, National Cheng Hsing University, Taichung, Taiwan

    Kuan-Cheng Lin

  4. Department of Business Administration, Overseas Chinese University Taichung, Taichung, Taiwan

    Jen-Chieh Wang

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  1. Min-Feng Lee
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  2. Guey-Shya Chen
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  3. Jason C. Hung
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  4. Kuan-Cheng Lin
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  5. Jen-Chieh Wang
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Correspondence to Min-Feng Lee.

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Lee, MF., Chen, GS., Hung, J.C. et al. Data mining in emotion color with affective computing. Multimed Tools Appl 75, 15185–15198 (2016). https://doi.org/10.1007/s11042-014-2231-8

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  • DOI: https://doi.org/10.1007/s11042-014-2231-8

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