A BERT-ABiLSTM Hybrid Model-Based Sentiment Analysis Method for Book Review

Abstract

Aiming at the problem of low accuracy rate of current sentiment analysis methods for book review texts, a book review sentiment analysis method based on BERT-ABiLSTM hybrid model is proposed. First, the overall framework of sentiment analysis is constructed by integrating sentiment vocabulary and deep learning methods, and the fine-grained sentiment analysis is divided into three stages: topic identification, sentiment identification and thematic sentiment identification. Then, a dynamic character-level word vector containing contextual information is generated using a bidirectional encoder representation from transformers (BERT) pre-trained language model. Then, the contextual information in the text data is fully learned by introducing the bidirectional long short-term memory (BiLSTM) model. Finally, the accurate analysis of book review sentiment is achieved by using Attention mechanism to highlight important features and improve the efficiency of resource utilization. Through an experimental comparison with existing advanced algorithms, the proposed method in this study has improved at least 4.2%, 3.9% and 3.79% in precision, recall and F1 values, respectively. The experimental results show that the proposed BERT-ABiLSTM is higher than the existing models under different metrics, indicating that the proposed model has a good application prospect in the fields of book review analysis and book recommendation.