基于交替注意力机制和图卷积网络的方面级情感分析模型

Aspect-level sentiment analysis model based on alternating‑attention mechanism and graph convolutional network

方面级情感分析旨在预测给定文本中特定目标的情感极性。针对忽略方面词和上下文之间的句法关系和平均池化带来的注意力差异性变小的问题,提出一种基于交替注意力(AA)机制和图卷积网络(GCN)的方面级情感分析模型(AA-GCN)。首先,利用双向长短期记忆(Bi-LSTM)网络对上下文和方面词进行语义建模;其次,通过基于句法依存树的GCN学习位置信息和依赖关系,再利用AA机制进行多层次交互学习,自适应地调整对目标词的关注度;最后,拼接修正后的方面特征和上下文特征,得到最终的分类依据。相较于基于目标依赖的图注意力网络(TDGAT),所提模型在4个公开数据集上准确率提升了1.13%~2.67%,在5个公开数据集上F1值提升了0.98%~4.89%,验证了利用句法关系和提升关键词关注度的有效性。

Aspect-level sentiment analysis aims to predict the sentiment polarity of specific target in given text.Aiming at the problem of ignoring the syntactic relationship between aspect words and context and reducing the attention difference caused by average pooling,an aspect-level sentiment analysis model based on Alternating-Attention(AA)mechanism and Graph Convolutional Network(AA-GCN)was proposed.Firstly,the Bidirectional Long Short-Term Memory(Bi-LSTM)network was used to semantically model context and aspect words.Secondly,the GCN based on syntactic dependency tree was used to learn location information and dependencies,and the AA mechanism was used for multi-level interactive learning to adaptively adjust the attention to the target word.Finally,the final classification basis was obtained by splicing the corrected aspect features and context features.Compared with the Target-Dependent Graph Attention Network(TD-GAT),the accuracies of the proposed model on four public datasets increased by 1.13%-2.67%,and the F1 values on five public datasets increased by 0.98%-4.89%,indicating the effectiveness of using syntactic relationships and increasing keyword attention.