Affective computing plays an important role in simulating human affects through multimedia stimuli. To provide appropriate responses and quantify emotions, it is essential to identify and interpret emotions accurately. Often the audience is influenced by media content and it has an impact on the audience's emotions. The authors aim to develop a machine vision based smart affective system that investigates the correlation between the basic facial expressions and corresponding ratings given by the audience. For the same, a notion of emotion vector is introduced to measure the elicited emotion. The idea of measuring attention is also suggested to find if the con- tent could keep the audience engaged. Our results reveal that such systems are useful in capturing and analyzing individual and collective sentiment.

情感计算在通过多媒体刺激模拟人类情感方面发挥着重要作用。为了提供适当的反应和量化情绪，准确识别和解释情绪至关重要。观众往往受媒体内容的影响，对观众的情绪产生影响。作者旨在开发一种基于机器视觉的智能情感系统，该系统研究基本面部表情与观众给出的相应评分之间的相关性。同样，引入了情绪向量的概念来测量引发的情绪。还建议使用衡量注意力的想法来确定内容是否可以保持观众的参与度。我们的结果表明，此类系统可用于捕获和分析个人和集体情绪。