A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis

Dongming Wu

Abstract:

Aspect-based-sentiment-analysis (ABSA) is a fine-grained sentiment evaluation task, which
analyzes the emotional polarity of the evaluation aspects. Generally, the emotional polarity
of an aspect exists in the corresponding opinion expression, whose diversity has great
impact on model’s performance. To mitigate this problem, we propose a novel and simple
counterfactual data augmentation method to generate opinion expressions with reversed
sentiment polarity. In particular, the integrated gradients are calculated to locate and mask
the opinion expression. Then, a prompt combined with the reverse expression polarity is
added to the original text, and a Pre-trained language model (PLM), T5, is finally was
employed to predict the masks. The experimental results shows the proposed counterfactual
data augmentation method performs better than current augmentation methods on three
ABSA datasets, i.e. Laptop, Restaurant, and MAMS.