BLR: A Multi-modal Sentiment Analysis Model

In multi-modal sentiment analysis tasks, deep learning plays an important role due to its excellent performance. Compared with the traditional statistical approaches and machine learning approaches, deep learning methods have better performance and stability. However, there are still two problems in multi-modal sentiment analysis works, one is that the fused features lead to missing important information. Another is that the affiliation of each feature after fusion is not precisely defined and calculated. To deal with issues, we propose a BLR Multi-channel dual FusionModel based on Bert,Lstm and ResNeST framework. Our model first ensures that the important features will not be lost after fusion to the maximum extent and then will be processed and optimized according to the contribution of each feature for the fusion. Finally, we conduct experiments on two datasets, the results show that the accuracy of our model gets 76.125% and 77.5%, growing 3.025% and 2.875% over the best baseline model, respectively. Thus, the proposed model BLR model, achieves better effectiveness in multi-modal sentiment analysis tasks.