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Aspect-term sentiment analysis (ATSA) is a longstanding challenge in naturallanguage understanding. It requires fine-grained semantical reasoning about atarget entity appeared in the text. As manual annotation over the aspects islaborious and time-consuming, the amount of labeled data is limited forsupervised learning. This paper proposes a semi-supervised method for the ATSAproblem by using the Variational Autoencoder based on Transformer (VAET), whichmodels the latent distribution via variational inference. By disentangling thelatent representation into the aspect-specific sentiment and the lexicalcontext, our method induces the underlying sentiment prediction for theunlabeled data, which then benefits the ATSA classifier. Our method isclassifier agnostic, i.e., the classifier is an independent module and variousadvanced supervised models can be integrated. Experimental results are obtainedon the SemEval 2014 task 4 and show that our method is effective with fourclassical classifiers. The proposed method outperforms two generalsemisupervised methods and achieves state-of-the-art performance.

Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer