Implementation of AI Deep Learning Algorithm For Multi-Modal Sentiment Analysis
This work addresses sentiment analysis for multi-modal data, but it appears incremental as it builds on existing deep learning techniques without introducing a major breakthrough.
The paper tackled multi-modal sentiment analysis by combining a two-channel CNN with a ring network and attention mechanisms, achieving improved recognition accuracy and reduced learning time on emotion datasets.
A multi-modal emotion recognition method was established by combining two-channel convolutional neural network with ring network. This method can extract emotional information effectively and improve learning efficiency. The words were vectorized with GloVe, and the word vector was input into the convolutional neural network. Combining attention mechanism and maximum pool converter BiSRU channel, the local deep emotion and pre-post sequential emotion semantics are obtained. Finally, multiple features are fused and input as the polarity of emotion, so as to achieve the emotion analysis of the target. Experiments show that the emotion analysis method based on feature fusion can effectively improve the recognition accuracy of emotion data set and reduce the learning time. The model has a certain generalization.