Another Dimension: Towards Multi-subnet Neural Network for Image Sentiment Analysis

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.