A novel Counterfactual method for aspect-based sentiment analysis

Aspect-based-sentiment-analysis (ABSA) is a fine-grained sentiment evaluation task, which analyze the emotional polarity of the evaluation aspects. Generally, the emotional polarity of an aspect exists in the corresponding opinion expression, whose diversity has great impacts on model's performance. To mitigate this problem, we propose a novel and simple counterfactual data augmentation method that reverses the opinion expression of the aspects. Specially, the integrated gradients are calculated to identify and mask the opinion expression. Then, a prompt with the reverse expression polarity is combined to the original text, and a pre-trained language model (PLM), T5, is finally was employed to predict the masks. The experimental results show the proposed counterfactual data augmentation method perform better than current methods on three open-source datasets, i.e. Laptop, Restaurant and MAMS.