Multimodal Sentiment Analysis Based on Interactive Transformer and Soft Mapping
Zuhe Li, Qingbing Guo, Chengyao Feng, Lujuan Deng, Qiuwen Zhang, Jianwei Zhang, Fengqin Wang, Qian Sun
Abstract
Multimodal sentiment analysis aims to harvest people’s opinions or attitudes from multimedia data through fusion techniques. However, existing fusion methods cannot take advantage of the correlation between multimodal data but introduce interference factors. In this paper, we propose an Interactive Transformer and Soft Mapping based method for multimodal sentiment analysis. In the Interactive Transformer layer, an Interactive Multihead Guided‐Attention structure composed of a pair of Multihead Attention modules is first utilized to find the mapping relationship between multimodalities. Then, the obtained results are fed into a Feedforward Neural Network. The Soft Mapping layer consisting of stacking Soft Attention module is finally used to map the results to a higher dimension to realize the fusion of multimodal information. The proposed model can fully consider the relationship between multiple modal pieces of information and provides a new solution to the problem of data interaction in multimodal sentiment analysis. Our model was evaluated on benchmark datasets CMU‐MOSEI and MELD, and the accuracy is improved by 5.57% compared with the baseline standard.