Book Rating Model Based on Self-Attention and LSTM

With the rapid development of Internet technology and online communication communities, text data has exploded. Emotion analysis of network information and research on the emotional tendency of users in various industries for products have become important research topics. Neural networks have good performance in the field of natural language processing. The traditional recurrent neural network has the problem of gradient disappearance. Therefore, this paper combines Self-Attention mechanism and LSTM model to realize the multi-classification of text emotional attributes and effectively obtain complete long sequence information. The experiment uses the Goodreads book review dataset for sentiment analysis. The experimental results show that the Self-Attention + LSTM model has a higher prediction accuracy than RNN. This proves that the model proposed in this paper can be used to improve the accuracy of text sentiment classification and has certain research value.