【Purpose/significance】Research on online users' sentiment analysis in emergency is mostly based on texts or texts com⁃

bined with images, lacking the research on the multimodal content of videos. At the same time, the existing multi-modal sentiment

analysis research mostly focuses on the feature extraction of different modalities, ignoring the context of videos and modal fusion.

【Method/process】Based on that, this paper proposes a sentiment analysis model based on the video context and high-dimensional fu⁃sion. The model first uses the Bi-GRU-based neural network to learn the context relationships of different modalities in the video, and then merges them in pairs. The fused features are inputted into the Bi-GRU network to learn the context relationships after the fusion of different modalities. Finally, a triple Cartesian product method is used to fuse bimodal features to capture the dynamic interaction between modalities to obtain high-dimensional fused features, which are inputted into multiple fully-connected layers to output la⁃bels.【Result/conclusion】The paper conducts empirical research on the proposed model in the real dataset of "COVID19" emergency,and compares it with the baseline models (TFN, HFCM, etc.). Experimental results show that the proposed model has certain advantag⁃es.【Innovation/limitation】The model and research method proposed in this paper can provide new ideas for the sentiment of online us⁃ers in emergencies.