A Optimized BERT for Multimodal Sentiment Analysis

Sentiment analysis of one modality (e.g., text or image) has been broadly studied. However, not much attention has been paid to the sentiment analysis of multi-modal data. As the research on and applications of multimodal data analysis are becoming more and more broad, it is necessary to optimize BERT internal structure. This article proposes a hierarchical multi-head self-attention and gate channel BERT, which is an optimized BERT model. The model is composed of three modules: the hierarchical multi-head self-attention module realizes the hierarchical extraction process of features; the gate channel module replaces BERT's original Feed Forward layer to realize information filtering; and the tensor fusion model based on a self-attention mechanism is utilized to implement the fusion process of different modal features. Experiments show that our method achieves promising results and improves accuracy by 5-6% when compared with traditional models on the CMU-MOSI dataset.