MSRL-Net: A multi-level semantic relation-enhanced learning network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of a given text towards several specific aspects. For implementing the ABSA, one way is to convert the original problem into a sentence semantic matching task, using pre-trained language models, such as BERT. However, for such a task, the intra-and inter-semantic relations among input sentence pairs are often not considered. Specifically, the semantic information and guidance of relations revealed in the labels, such as positive, negative and neutral, have not been completely exploited. To address this issue, we introduce a self-supervised sentence pair relation classification task and propose a model named Multi-level Semantic Relation-enhanced Learning Network (MSRL-Net) for ABSA. In MSRL-Net, after recasting the original ABSA task as a sentence semantic matching task, word dependency relations and word-sentence relations are utilized to enhance the word-level semantic representation for the sentence semantic matching task, while sentence semantic relations and sentence pairs relations are utilized to enhance the sentence-level semantic representation for sentence pair relation classification. Empirical experiments on SemEval 2014 Task 4, SemEval 2016 Task 5 and SentiHood show that MSRL-Net significantly outperforms other baselines such as BERT in terms of accuracy, Macro-F1 and AUC.

Automated summary

This paper introduces an improved model, MSRL-Net, for sentiment analysis task, which enhances sentence-level semantic representation by utilizing semantic relations between sentences. Compared to baseline models like BERT, MSRL-Net achieves significant improvements in accuracy, Macro-F1, and AUC.