  Aspect-based sentiment analysis (ABSA), a fine-grained sentiment
classification task, has received much attention recently. Many works
investigate sentiment information through opinion words, such as ''good'' and
''bad''. However, implicit sentiment widely exists in the ABSA dataset, which
refers to the sentence containing no distinct opinion words but still expresses
sentiment to the aspect term. To deal with implicit sentiment, this paper
proposes an ABSA method that integrates explicit sentiment augmentations. And
we propose an ABSA-specific augmentation method to create such augmentations.
Specifically, we post-trains T5 by rule-based data. We employ Syntax Distance
Weighting and Unlikelihood Contrastive Regularization in the training procedure
to guide the model to generate an explicit sentiment. Meanwhile, we utilize the
Constrained Beam Search to ensure the augmentation sentence contains the aspect
terms. We test ABSA-ESA on two of the most popular benchmarks of ABSA. The
results show that ABSA-ESA outperforms the SOTA baselines on implicit and
explicit sentiment accuracy.
