Combining Syntactic and Position Relation for Targeted Sentiment Analysis Using Graph Neural Network

Abstract

Targeted sentiment analysis aims to predict the sentiment polarity of the target in a sentence. Most traditional Graph Neural Network-based methods have focused only on the syntactic dependency information of sentences. However, they ignore the position information of words in the linear form of sentences, which leads the model to paying attention to the irrelevant syntactic dependency information to the target. To attenuate the irrelevant information, a novel model called Position-aware Dual Relational Graph Attention Network (PDRGAT) is proposed. Firstly, introducing the position-aware weight window of the syntactic dependency information to make the model pay more attention to the local syntactic information of words that neighbor the target. Secondly, a dual relational attention mechanism combining syntactic and position information is proposed. Experiments show that our model can effectively attenuate the irrelevant syntactic information and outperform state-of-the-art baselines on Accuracy and Macro-F1.