A Multi-feature Fusion Approach Based on Domain Adaptive Pretraining for Aspect-based Sentiment Analysis

Abstract

Abstract Aspect-based sentiment classification (ASC) is a popular task that aims to identify the corresponding emotion of a specific aspect for aspect-based sentiment analysis. Dependency parsing is currently considered as an efficient tool for recognizing the opinion words in the sentiment text. However, many dependency-based methods might be susceptible to the dependency tree and inevitably introduce noisy information and neglect the rich relation information between words. In this paper, we propose a multi-feature fusion approach based on domain adaptive pretraining for ASC and reducing dependency noisy information. We use the Multi-task Learning (MTL) technique for domain adaptive pretraining, which combines Biaffine Attention Model (BAM) and Mask Language Model (MLM) by jointly considering the structure, relations of edges, and linguistic features in the sentiment text. The pretrained dependency graph will be input into a double graph fusion-based message passing neural network (MPNN) that is initialized with the optimal parameters of the pretrained BAM for MPNN training, which fully considers these different features that are affected with each other for ASC. Extensive experiments were made on four benchmark datasets for comparing our approach with the state-of-the-art ASC approaches, and the results show that our model is very competitive in the ASC task compared with the state-of-the-art alternatives.