Multimodal Sentiment Analysis Using Multi-tensor Fusion Network with Cross-modal Modeling
Xueming Yan, Haiwei Xue, Shengyi Jiang, Ziang Liu
Abstract
With the rapid development of social networks, more and more people express their emotions and opinions via online videos. However, most of the current research on multimodal sentiment analysis cannot do well with effective emotional fusion in multimodal data. To deal with the problem, we propose a multi-tensor fusion network with cross-modal modeling for multimodal sentiment analysis. In this study, the multimodal feature extraction with cross-modal modeling is utilized to obtain the relationship of emotional information between multiple modalities. Moreover, the multi-tensor fusion network is used to model the interaction of multiple pairs of bimodal and realize the emotional prediction of multimodal features. The proposed approach performs well in regression and different dimensions of classification experiments on the two public datasets CMU-MOSI and CMU-MOSEI.