基于词共现的方面级情感分析模型

针对方面级情感分析存在的局部信息捕捉不充分、多个意见词混淆的问题，提出了一种基于词共现的方面级情感分析模型。该模型将方面级情感分析看成句子对任务，利用BERT获得包含上下文与方面词交互注意力的节点信息；同时，对每条数据样本构建独立的词共现图，使用门控图神经网络更新节点，加强方面词附近信息的融合，减少无关意见词的干扰；之后在自注意力层进一步融合全局信息，最终取出方面词节点送入非线性层获得分类结果。与6个基线模型的对比实验结果表明，该模型有效地提升了方面级情感分析的准确性。

An aspect-level sentiment analysismodel based on word co-occurrence

Aiming at the problems of insufficient local information capture and multiple opinion words confusion in aspect level sentiment analysis, this paper proposes an aspect level sentiment analysis model based on word co-occurrence. In this model, aspect level sentiment analysis is regarded as a sentence pair task, and the node information including context and aspect word interaction attention is obtained by BERT. At the same time, independent word co-occurrence graph is constructed for each data sample, and gating graph neural network is used to update nodes to enhance the fusion of information near aspect words and reduce the interference of irrelevant opinion words. Then, the global information is further fused in the self-attention layer. Finally, the aspect word nodes are sent to the nonlinear layer to obtain the classification results. Compared with six baseline models, the experimental results show that the model can effectively improve the accuracy of aspect level sentiment analysis.