Multimodal Sentiment Analysis Using Multi-tensor Fusion Network with Cross-modal Modeling

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.

Acknowledgments

Thanks to Yifeng Tan for revising the English grammar of the paper.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported in part by the Guangzhou Science and technology project [202102020878], the National Natural Science Foundation of China [62006053], the Special Innovation Project of Guangdong Education Department [2018KQNCX072].