A sentiment classification method for Weibo sensitive topic text based on multimodal features

Due to the problem of reduced classification accuracy in traditional text sentiment classification methods, this paper proposes a Weibo sensitive topic text sentiment classification method based on multimodal features. Firstly, the bidirectional loop structure is introduced to improve the GRU model, and a BiGRU model is constructed for multimodal feature extraction and fusion of sensitive topics on Weibo. Secondly, by combining seed features, similar features, and residual features, a multimodal feature cluster is constructed to improve the accuracy of classification. Finally, the constructed multimodal feature clusters are input into the support vector machine model to complete sentiment classification of Weibo sensitive topic text. The experimental results show that compared with traditional methods, our method achieves higher accuracy in all emotion categories.