Department of Emotion Engineering, University of Sangmyung, Seoul 03016, Korea.
Department of Human-Centered Artificial Intelligence, University of Sangmyung, Seoul 03016, Korea.
Sensors (Basel). 2020 Jan 24;20(3):649. doi: 10.3390/s20030649.
The increasing interest in the effects of emotion on cognitive, social, and neural processes creates a constant need for efficient and reliable techniques for emotion elicitation. Emotions are important in many areas, especially in advertising design and video production. The impact of emotions on the audience plays an important role. This paper analyzes the physical elements in a two-dimensional emotion map by extracting the physical elements of a video (color, light intensity, sound, etc.). We used k-nearest neighbors (K-NN), support vector machine (SVM), and multilayer perceptron (MLP) classifiers in the machine learning method to accurately predict the four dimensions that express emotions, as well as summarize the relationship between the two-dimensional emotion space and physical elements when designing and producing video.
人们对情绪对认知、社会和神经过程的影响越来越感兴趣，这就产生了对高效可靠的情绪诱发技术的持续需求。情绪在许多领域都很重要，尤其是在广告设计和视频制作中。情绪对观众的影响起着重要的作用。本文通过提取视频的物理元素（颜色、光强、声音等），分析二维情绪图中的物理元素。我们在机器学习方法中使用 k-最近邻（K-NN）、支持向量机（SVM）和多层感知器（MLP）分类器，准确预测表达情绪的四个维度，并总结设计和制作视频时二维情绪空间与物理元素之间的关系。
