Abstract
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.
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