Aspect-Based Sentiment Analysis Method Based on Knowledge Enhancement and Multi-Layer Attention Mechanism
Aspect-level sentiment analysis aims to analyze the corresponding sentimental polarity of different aspects in long sentences.Currently,newer neural network models primarily concentrate on the impact of aspect term extraction on sentiment prediction but overlook real text defects such as ambiguity and lack of textual information.This oversight leads to inadequate exploration of the complementarity between external knowledge and aspect term information,leaving room for further optimization of model performance.To solve this problem,this paper proposes a method based on knowledge enhancement and a multi-layer attention mechanism.The model stores text representation and auxiliary information through a bi-directional long short-term memory network,obtains syntax information using a graph convolutional network,and extracts categorical representation of aspect terms by knowledge enhancement.This means a multi-layer attention mechanism is introduced to input the fused features into the global average pooling layer to improve accuracy.On the SemEval task and Twitter dataset,the proposed model outperforms the baseline and achieves good experimental results.
NETL
NSTL
万方数据
