Ensemble Graph Convolutional Networks for Improving the Performance of Aspect‐Level Sentiment Analysis

ABSTRACT Aspect‐level sentiment analysis (ALSA) is the process of determining the emotional polarity that people have towards aspects of topics or entities expressed in their opinions. ALSA is increasingly integrated into many practical applications to make them more user‐friendly and suitable for users' psychological and emotional trends. Therefore, the performance of ALSA methods is increasingly being studied by scientists for improvement. Various approaches have been proposed for ALSA, the latest of which is Graph Convolutional Networks (GCNs). Although they have performed well, previous GCN‐based methods still fail to capture all important features from opinions. This raises the question of whether combining ALSA‐based GCNs can improve the ability of previous methods to capture important features. This motivates us to propose the ALSA method based on the Ensemble Graph Convolutional Networks (EGCNs). The objective of the proposed method is to capture features in a manner that is both independent and joint, in order to leverage the advantages of jointly learning features while also benefiting from the strengths of learning features independently. The proposed method includes the following main steps: (i) data representation based on the BERT model; (ii) extracting syntactic, semantic and contextual features based on the ASGCN, ATGCN and ASCNN models, respectively; (iii) combining the extracted feature vectors into a general feature vector based on the fusion mechanism; (iv) sentiment analysis based on the Softmax function. To demonstrate the performance of the EGCNs model, it is experimented on three benchmark datasets and compared with the previous methods before being combined.