Multi-Task Learning Model Based on BERT and Knowledge Graph for Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment of an aspect in a given sentence and thus can provide people with comprehensive information. However, many conventional methods need help to discover the linguistic knowledge implicit in sentences. Additionally, they are susceptible to unrelated words. To improve the performance of the model in the ABSA task, a multi-task sentiment analysis model based on Bidirectional Encoder Representation from Transformers (BERT) and a Knowledge Graph (SABKG) is proposed in this paper. Expressly, part-of-speech information is incorporated into the output representation of BERT, thereby obtaining textual semantic information through linguistic knowledge. It also enhances the textual representation to identify the aspect terms. Moreover, this paper constructs a knowledge graph of aspect and sentiment words. It uses a graph neural network to learn the embeddings in the triplet of “aspect word, sentiment polarity, sentiment word”. The constructed graph improves the contextual relationship between the text’s aspect and sentiment words. The experimental results on three open datasets show that the proposed model can achieve the most advanced performance compared with previous models.