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Multimodal Emotion Analysis Based on PCA Processing Feature Set

Multimodal affective analysis has become an increasingly popular research direction. Different from single mode, modal fusion in multi-mode is a key problem. In this paper, the feature sets obtained by several different feature extraction algorithms are used, and then principal component analysis (PCA) is used to reduce the dimension to improve the effect. Two PCA algorithms are adopted in this paper. They are based on the eigenvalue decomposition covariance matrix and SVD decomposition covariance matrix, respectively. Compared with the original feature set, the effect has been significantly improved so that our model can train and test better results. After that, we use the way of data mosaic to fuse the video, audio and text features in the early stage. We can get ideal results by combining the results learned from more than two modes and applying them to the analysis and prediction of multimodal emotion.