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.
@article{arxiv.2111.02194,
title = {Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training},
author = {Zhengyan Li and Yicheng Zou and Chong Zhang and Qi Zhang and Zhongyu Wei},
journal= {arXiv preprint arXiv:2111.02194},
year = {2021}
}
Comments
Accepted as a long paper in the main conference of EMNLP 2021