  Aspect-term sentiment analysis (ATSA) is a longstanding challenge in natural
language understanding. It requires fine-grained semantical reasoning about a
target entity appeared in the text. As manual annotation over the aspects is
laborious and time-consuming, the amount of labeled data is limited for
supervised learning. This paper proposes a semi-supervised method for the ATSA
problem by using the Variational Autoencoder based on Transformer (VAET), which
models the latent distribution via variational inference. By disentangling the
latent representation into the aspect-specific sentiment and the lexical
context, our method induces the underlying sentiment prediction for the
unlabeled data, which then benefits the ATSA classifier. Our method is
classifier agnostic, i.e., the classifier is an independent module and various
advanced supervised models can be integrated. Experimental results are obtained
on the SemEval 2014 task 4 and show that our method is effective with four
classical classifiers. The proposed method outperforms two general
semisupervised methods and achieves state-of-the-art performance.

Computation and Language