A novel Counterfactual method for aspect-based sentiment analysis
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Aspect-based-sentiment-analysis (ABSA) is a fine-grained sentiment evaluation task, which analyze the emotional polarity of the evaluation aspects. However, previous works only focus on the identification of opinion expressions, forget that the diversity of opinion expressions also has great impacts on the ABSA task. To mitigate this problem, we propose a novel counterfactual data augmentation method to generate opinion expression with reversed sentiment polarity. Specially, the integrated gradients are calculated to identify and mask the opinion expression. Then, a prompt with the reverse label is combined to the original text, and a pre-trained language model (PLM), T5, is finally employed to retrieve the masks. The experimental results show the proposed counterfactual data augmentation method perform better than current augmentation methods on three ABSA datasets, i.e. Laptop, Restaurant and MAMS.
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