Sentiment analysis of film reviews based on CNN-BLSTM-Attention

Abstract

In order to accurately analyse the emotional tendency of film reviews, help investors make decisions and improve the quality of works, an optimized CNN-BLSTM-Attention sentiment analysis model was designed. The CNN model has a strong ability to capture the local correlation of spatial or temporal structures. The RNN model can either process sequences of any length or capture long-range dependencies, but it is easy to cause the problem of gradient disappearance. The CNN-BLSTM-Attention sentiment analysis model designed in this paper, which combines the advantages of CNN and RNN, is more accurate when being used to analyze the sentiment characteristics of texts. The experimental results show that the accuracy of the CNN-BLSTM-Attention after optimization model is better than that of CNN and RNN models in the experiment, which proves the effectiveness of the analysis method in this paper and can provide some significance for the optimization of related sentiment analysis models.