AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model.

ABSTRACT: Multimodal sentiment analysis is an essential task in natural language processing which refers to the fact that machines can analyze and recognize emotions through logical reasoning and mathematical operations after learning multimodal emotional features. For the problem of how to consider the effective fusion of multimodal data and the relevance of multimodal data in multimodal sentiment analysis, we propose an attention-based mechanism feature relevance fusion multimodal sentiment analysis model (AFR-BERT). In the data pre-processing stage, text features are extracted using the pre-trained language model BERT (Bi-directional Encoder Representation from Transformers), and the BiLSTM (Bi-directional Long Short-Term Memory) is used to obtain the internal information of the audio. In the dat

SUBMITTER: Mingyu J 

PROVIDER: S-EPMC9462790 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature