Semi-supervised Target-level Sentiment Analysis via Variational Autoencoder

Target-level aspect-based sentiment analysis (TABSA) is a long-standing challenge, which requires fine-grained semantical reasoning about a certain aspect. 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 TABSA problem based on the Variational Autoencoder (VAE). VAE is a powerful deep generative model which models the latent distribution via variational inference. By disentangling the latent representation into the aspect-specific sentiment and the context, the method implicitly induces the underlying sentiment prediction for the unlabeled data, which then benefits the TABSA 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 semi-supervised methods and achieves competitive performance.