Sentiment Analysis of Movie Reviews based on Pretraining and Dual Branch Coding

Due to the high dimensionality and sparsity of text and the complex semantics of the natural language, sentiment analysis of movie reviews presents difficult challenges. To solve these problems, a novel architecture that contains a robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa), a bidirectional long short-term memory (BiLSTM), a temporal convolutional network (TCN) and a convolutional layer is proposed in this paper. The proposed architecture is called dual branch feature coding network based on RoBERTa (DBN-Ro). In DBN-Ro, the RoBERTa pretraining model is used to obtain the word embedding and the dual branch network is used to extract the contextual semantic and multi-level representations. The convolutional layer is used to perform dimensionality compression on the stitched vectors. The DBN-Ro is experimentally validated on two movie reviews, the results show that it achieves better prediction results in sentiment analysis of movie reviews tasks.