Analysis of sentiment analysis model based on deep learning
Guanchao Wang · Applied and Computational Engineering · 2023
A traditional yet important topic in the study of natural language processing is sentiment analysis. Deep learning models have gradually taken over as one of the primary techniques for resolving sentiment analysis issues over the last ten years. Common deep learning models targeting sentiment analysis tasks include both recurrent and convolutional neural networks, as well as the BERT model. The current research examines the classificational accuracy of numerous deep learning models with diverse structural types in order to compare their performance in sentiment analysis. Results from the experiments suggest that the pre-training BERT model achieves the highest accuracy, while the convolutional neural network appears to sustain better results on sentiment analysis than standard recurrent neural networks.