Abstract
The study of aspect-level sentiment analysis using deep learning methods is one of the more important research directions in the field of natural language processing in recent years. In this paper, we address the problem of insufficient extraction of deep semantic features in existing aspect-level sentiment analysis research, design and build a sentiment analysis model based on the pre-trained language model BERT, fuse BiLSTM and GCN deep learning methods, analyze the sentiment tendency on the collected product review dataset, design relevant experiments to compare in the same application scenario, and verify the effectiveness of the proposed model.
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