Aspect-Level Sentiment Classification Based on Self-Attention Routing via Capsule Network

Abstract

Aspect-level sentiment classification task aims at determining the sentiment polarity towards each aspect in a sentence. Although existing models have achieved remarkable performance, they always ignore the semantic relationship between aspects and their context, resulting in the lack of syntax information and aspect features. Therefore, the paper proposes a novel model named ASC based on the Self-Attention routing combined with the Position-biased weight approach, ASC-SAP in short. First, the paper utilizes the position-biased weight approach to construct an aspect-enhanced embedding. Furthermore, the paper develops a novel non-iterative but highly parallelized self-attention routing mechanism to efficiently transfer the aspect features to the target capsules. In addition, the paper utilizes pre-trained model bidirectional encoder representation from transformers (BERT). Comprehensive experiments show that our model achieves excellent performance on Twitter and SemEval2014 benchmarks and verify the effectiveness of our models.