Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training

TL;DR

This paper introduces a supervised contrastive pre-training approach to improve aspect-based sentiment analysis, especially for implicit sentiments, achieving state-of-the-art results on benchmark datasets.

Contribution

It proposes a novel supervised contrastive pre-training method that effectively captures implicit sentiment expressions in reviews, enhancing sentiment analysis performance.

Findings

Achieves state-of-the-art results on SemEval2014 benchmarks.

Effectively captures implicit sentiment expressions.

Improves sentiment analysis accuracy for reviews without explicit opinion words.

Abstract

Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews. We notice that about 30% of reviews do not contain obvious opinion words, but still convey clear human-aware sentiment orientation, which is known as implicit sentiment. However, recent neural network-based approaches paid little attention to implicit sentiment entailed in the reviews. To overcome this issue, we adopt Supervised Contrastive Pre-training on large-scale sentiment-annotated corpora retrieved from in-domain language resources. By aligning the representation of implicit sentiment expressions to those with the same sentiment label, the pre-training process leads to better capture of both implicit and explicit sentiment orientation towards aspects in reviews. Experimental results show that our method achieves state-of-the-art performance on SemEval2014 benchmarks, and comprehensive analysis validates its effectiveness on learning implicit sentiment.