Graph Attention Networks for Multiple Pairs of Entities and Aspects Sentiment Analysis in Long Texts

The goal of sentiment analysis is to detect the opinion polarities of people towards specific targets.For finegrained analysis aspect-based sentiment analysis(ABSA)is a challenging subtask of sentiment analysis The goals of most literature are to judge sentiment orientation for a single aspect,but the entities aspects belong to are ignored.Sequence-based methods,such as LSTM,or tagging schemas,such as BIO,always rely on relative distances to target words or accurate positions of targets in sentences It will require more detailed annotations if the target words do not appear in sentences.In this paper,we discuss a scenario where there are multiple entities and shared aspects in multiple sentences.The task is to predict the sentiment polarities of different pairs,ie,(entity,aspect)in each sample and the target entities or aspects are not guaranteed to exist in texts.After converting the long sequences to dependency relation-connected graphs,the dependency distances are embedded automatically to generate contextual representations during iterations We adopt partly densely connected graph convolutional networks with multi-head attention mechanisms to judgethe sentiment polarities for pairs of entities and aspects.The experiments conducted onaChinesedataset demonstrate the effectiveness of the method.Wealso explore the influences of different attention mechanisms and the connection manners of sentences on the tasks.