Context-Dependent Multimodal Sentiment Analysis Based on a Complex Attention Mechanism

Abstract

Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%.

Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion

“…Inspired by CBAM [29] and CA [30], this study proposes the Three-dimensional Coordinate Attention Mechanism (TDCA) to enhance features. Previous work has already demonstrated that information in both channel and spatial dimensions of feature maps is highly significant [31]. CBAM combines the channel attention mechanism and spatial attention mechanism for the first time.…”