  Because multimodal data contains more modal information, multimodal sentiment
analysis has become a recent research hotspot. However, redundant information
is easily involved in feature fusion after feature extraction, which has a
certain impact on the feature representation after fusion. Therefore, in this
papaer, we propose a new multimodal sentiment analysis model. In our model, we
use BERT + BiLSTM as new feature extractor to capture the long-distance
dependencies in sentences and consider the position information of input
sequences to obtain richer text features. To remove redundant information and
make the network pay more attention to the correlation between image and text
features, CNN and CBAM attention are added after splicing text features and
picture features, to improve the feature representation ability. On the
MVSA-single dataset and HFM dataset, compared with the baseline model, the ACC
of our model is improved by 1.78% and 1.91%, and the F1 value is enhanced by
3.09% and 2.0%, respectively. The experimental results show that our model
achieves a sound effect, similar to the advanced model.
