Multimodal Sentiment Analysis Model Based on CLIP and Cross-attention

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Multimodal Sentiment Analysis Model Based on CLIP and Cross-attention

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[1]CHEN Yan,LAI Yubin,XIAO Ao,et al.Multimodal Sentiment Analysis Model Based on CLIP and Cross-attention[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):42-50.[doi:10.13705/j.issn.1671-6833.2024.02.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 02 Page number: 42-50 Column: Public date: 2024-03-06

Title:: Multimodal Sentiment Analysis Model Based on CLIP and Cross-attention

Author(s):: CHEN Yan¹; 2; LAI Yubin¹; XIAO Ao¹; LIAO Yuxiang¹; CHEN Ningjiang¹; 1. School of Computer and Electronic Information Science, Guangxi University, Nanning 530000, China; 2. Guangxi Key Laboratory of Multimedia Communication and Network Technology, Guangxi University, Nanning 530000, China

Keywords:: sentiment analysis; multimodal learning; cross-attention; CLIP model; Transformer; feature fusion

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2024.02.003

Abstract:: In response to the issues of limited annotated data, insufficient fusion between modalities, and information redundancy in multimodal sentiment analysis, a multimodal sentiment analysis model called CLIP-CA-MSA based on contrastive language-image pretraining(CLIP) and cross-attention mechanism was proposed in this study. This model employed models such as BERT which was pre-trained by CLIP, and PIFT to extract feature vectors from videos and textual content. Subsequently, a cross-attention mechanism was applied to facilitate interaction between image feature vectors and text feature vectors, enhancing information exchange across different modalities. Finally, the uncertainty loss was utilized to compute the fused features, and the ultimate sentiment classification results were generated from the outputs. The experimental results showed that the model could increase accuracyrate by 5 percentage points to 14 percentage points and the F1 value by 3 percentage point to 12 percentage point over other multimodal models, which verifieed the superiority of the model in this study. And uses of ablation experiments to verified the validity of each module of the model. This model could effectively utilize the complementarity and correlation of multimodal data, and utilize uncertainty loss to improve the robustness and generalization ability of the model.

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Last Update: 2024-03-08