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.

采用超级对比预训练的方法，利用语言资源中的大规模情感注释语料库，对于产品评论中蕴含的明示和隐含情感表达进行预测，大幅提高对于细节方面表达情感的识别率，这种方法不仅在论文中实验证明了其有效性，还在 SemEval2014 基准测试集上取得了最好的表现。