Research on Microblog Sentiment Analysis Based on Multimodal and Multiscale Feature Fusion

Abstract

The pneumonia epidemic spread by the 2019 new coronavirus(2019-nCoV) has affected people's lives in any aspects, and has aroused widespread concern in global public opinion. In order to better grasp the real public opinion situation on the Internet and ensure the progress of epidemic prevention and public opinion analysis, this paper conducts research on netizen sentiment analysis for epidemic-related topics in the Internet community, and proposes a multimodal feature fusion solution. For the fusion of image and text modalities, Bi-LSTM and Bi-GRU are used to further learn the intrinsic correlation between modalities on the basis of bidirectional transformer feature fusion, and an image-based multi-scale feature fusion method is proposed, which can better solve the problem in this task. Experiments show that the method proposed in this paper is better than the current mainstream multimodal sentiment analysis methods.