Hybrid Graph Neural Network-Based Aspect-Level Sentiment Classification

Abstract

Aspect-level sentiment classification has received more and more attention from both academia and industry due to its ability to provide more fine-grained sentiment information. Recent studies have demonstrated that models incorporating dependency syntax information can more effectively capture the aspect-specific context, leading to improved performance. However, existing studies have two shortcomings: (1) they only utilize dependency relations between words, neglecting the types of these dependencies, and (2) they often predict the sentiment polarity of each aspect independently, disregarding the sentiment relationships between multiple aspects in a sentence. To address the above issues, we propose an aspect-level sentiment classification model based on a hybrid graph neural network. The core of our model involves constructing several hybrid graph neural network layers, designed to transfer information among words, between words and aspects, and among aspects. In the process of information transmission, our model takes into account not only dependency relations and their types between words but also sentiment relationships between aspects. Our experimental results based on three commonly used datasets demonstrate that the proposed model achieves a performance that is comparable to or better than recent benchmark methods.