Context-based interactive multimodal sentiment analysis

Ying Ou; Changhui Liu; Hao Liu

doi:10.1117/12.2640851

13 October 2022 Context-based interactive multimodal sentiment analysis

Ying Ou, Changhui Liu, Hao Liu

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228711 (2022) https://doi.org/10.1117/12.2640851
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

In view of the singleness of traditional text emotion analysis, multimodal emotion analysis extends emotion analysis to the analysis level of text, image, sound and so on. In order to use the context interaction information expressed in each modality, a multimodal interactive emotion classification model based on video context is proposed in this paper. The ALBERTBiGRU network structure is built for text feature learning, and the independent BIGRU model is used to extract context features from text, audio and video modality. Based on the attention mechanism, the emotion analysis task is completed after the fusion of the three modal features. Compared with the existing models on MOSI and IEMOCAP data sets, the accuracy and F1 value of emotion classification reached 81.71% and 81.44 on MOSI data set, 66.97% and 67.20 on IEMOCAP data set, which were 1.41% and 2.16% higher than the highest benchmark value respectively, effectively improving the accuracy of multimodal emotion prediction.

Citation Download Citation

Ying Ou, Changhui Liu, and Hao Liu "Context-based interactive multimodal sentiment analysis", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228711 (13 October 2022); https://doi.org/10.1117/12.2640851

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