Aspect-level sentiment analysis is a fine-grained sentiment analysis task.Most of the existing graph convolutional network-based models only consider the interaction between specific aspects and contexts,largely ignoring the interactive emotional features between aspects.Aiming at this deficiency,a model (MHSAGCN-BERT) that utilizes pre-trained BERT and a Multi-head Self-attention mechanism (MHSA) combined with a graph convolutional network is proposed.The syntactic dependencies of aspect words and context and the mutual sentiment relationship between aspects are used to derive aspect-specific sentiment polarity,thereby enhancing the model's ability to learn features.The experimental results on three public datasets of international semantic evaluation show that the model is significantly improved compared with other aspect-level sentiment analysis models.
关键词
方面级情感分析/多头自注意力机制/图卷积网络/方面交互/句法依赖树
Key words
aspect-level sentiment analysis/multi-head self-attention mechanism/graph convolutional networks/aspect interaction/syntactic dependency tree
维普
万方数据