Abstract
Image sentiment analysis has been studied for many years, and most of algorithms take the image sentiment as independent and discrete labels to predict by machine learning. Actually, as a product of multiple hormone combinations, emotions are generated by mutual suppression signal in brain. Inspired by neural microcircuit in amygdala, we propose a novel Multi-Subnet Neural Network (MSNN) that simulates the human brain mechanism for image sentiment classification. Different from traditional neural network, MSNN extends a new domain channel to imitate the way that images stimulate the brain through different neural circuits and produce sentimental semantic information by multi-subnet and signal reforming network. Experiments show that MSNN is well adapted to multi-class image sentiment classification task, and outperforms other multi-class sentiment classification models.
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