Abstract
The goal of Target Sentiment Analysis (TSA) is to predict the users’ sentiment towards specific targets from review sentences. However, the predicting results may not perform well due to the sparsity of training data. Data augmentation is a fruitful technology to alleviate the influence of imperfect training data, which obtains additional data by transforming the original samples. Unfortunately, there is hardly a particular data augmentation approach for TSA. To address this problem, in this paper, we propose a low-cost and effective data augmentation method called SubCrime, which constructs auxiliary sentences in two steps: Substitute and disCriminate. The former aims to substitute reasonable targets for the observed sentences through the masked language model, while the latter discriminates the restructured sentences via the constrained objective. SubCrime does not require extra knowledge and tedious manual annotation. We design SubCrime to answer the key counterfactual question: “If the review target in the sentence changed, would its sentiment be different ?”. Experiments show SubCrime improves on average 2 to 4 points in F1 scores on four datasets compared to methods without enhancement. Moreover, SubCrime also outperforms other data augmentation methods widely used in other Natural Language Processing (NLP) tasks.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of Chongqing, China under Grant cstc2021jcyi-bshX0168, the Intelligent Medical Project of Chongqing Medical University under Grant ZHYXQNRC202101, and Graduate Research and Innovation Foundation of Chongqing, China (Grant No.CYC21072).
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Chen, W., Wang, L., Du, J., He, Z. (2022). SubCrime: Counterfactual Data Augmentation for Target Sentiment Analysis. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_26
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