Ensemble correction model for aspect-level sentiment classification

Abstract

The aspect-level sentiment analysis is widely used in public opinion analysis. However, the problem of context information loss and distortion with the increase of the model depth is rarely considered in previous research. Few studies have attempted to combine the feature extracted from different embedding models. Based on the correction strategy, the ensemble correction (EC) model proposed in this study can correct context information loss and distortion. Based on the ensemble learning strategy and the weight sharing strategy, EC can extract features from different word embedding models and can reduce computational complexity. Experiments on the resturant14, laptop14, resturant16 and twitter datasets show that the accuracies of the EC model are 0.8848, 0.8213, 0.9301 and 0.7731, respectively. The accuracy of the EC model is higher than state-of-the-art models. Ablation studies and case studies are used to verify the model structure. The optimal number of graph convolutional network (GCN) layers is also verified.