Editorial for the special issue on "Research on methods of multimodal information fusion in emotion recognition"
Personal and Ubiquitous Computing
Emotion recognition is a significant branch of affective computing and a research direction highlighted in the field of artificial intelligence, human-computer interaction, and pattern recognition, etc. Emotion recognition, one of multidisciplinary research subjects related to computer science, mathematics, physiology, and psychology, is still faced with a series of problems to be solved, especially the problems about feature extraction, dimension reduction, recognition algorithms, and
... l information fusion. Speech and facial expression are two important external channels used in many studies in emotion recognition, but both of these channels cannot represent the real and inner emotional experience. A large number of studies have reported that satisfactory performance of emotion recognition can be achieved by physiological signals under laboratory conditions. However, there are inevitably inherent flaws of single-modality emotion recognition in the aspect of recognition accuracy and stability. To compensate for the defect of single modalities, multimodal emotion recognition has emerged and obtained great attention. Recently, multimodal information fusion used in emotion recognition is still in an exploring stage with immature methods and techniques, thus the exploration in this field should be further deeply and thoroughly. The aim of this special issue is to capture recent research and seek contributions of high-quality papers in this field. Under the support of related worldwide researchers, 47 papers have been received. Based on the review comments from peer reviewers, 25 papers have been selected out for the special issue and authors have revised their paper according to the comments before the final acceptance. The 25 paper which cover broad topics are introduced briefly as follows. The paper "Emotional computing based on cross-modal fusion and edge network data incentive" presented an emotional computing algorithm based on cross-modal fusion and edge network data incentive. The paper "Hot news mining and public opinion guidance analysis based on sentiment computing in network social media" proposed the dictionary supervised emotion computing model, which can be applied in hot news mining and public opinion guidance analysis based on sentiment computing in network social media. In the paper "Multimodal emotion recognition algorithm based on edge network emotion element compensation and data fusion", studied the multi-modal emotion recognition algorithm based on emotion element compensation in the background of streaming media communication in edge network. In the paper "Optimal path planning for two-wheeled selfbalancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm", an optimal path planning of two-wheel self-balancing pendulum robot is proposed based on the self-balance of free-floating two-wheel selfbalancing pendulum robot system. In the paper "Network text sentiment analysis method combining LDA text representation and GRU-CNN", a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. In this paper, "Multi-source heterogeneous data fusion based on perceptual semantics in narrow-band Internet of Things", a multi-source heterogeneous data fusion based on perceptual semantics in NB-IoT is proposed in this paper. The experiment has shown that our proposed algorithm has faster convergence rate, higher stability, and its judgment to fusion results are more suitable to actual conditions. The paper "A multiobjective evolutionary algorithm based on surrogate individual selection mechanism", proposed a