Semi-supervised target-oriented sentiment classification

Abstract

Target-oriented aspect-based sentiment analysis (TABSA) is a sentiment classification task that requires performing fine-grained semantical reasoning about a given aspect. The amount of labeled data is usually insufficient for supervised learning because the manual annotation w.r.t. the aspects is both time-consuming and laborious. In this paper, we propose a novel semi-supervised method to derive and utilize the underlying sentiment of unlabeled samples via a deep generative model. This method assumes that when given the aspect, the sentence is generated by two stochastic variables, i.e., the context variable and the sentiment variable. By explicitly disentangling the representation into the context and sentiment, the meaning of sentiment variable can be kept clean during the training phase. An additional advantage is that the proposed method uses a standalone classifier, and as such, our system is able to integrate with various supervised models. In terms of the implementation, since capturing the conditional input is non-trivial for a sequential model, special structures are put forward and investigated. We conducted experiments on SemEval 2014 task 4 and the results indicate that our method effectively handles five kinds of advanced classifiers. The proposed method outperforms two general semi-supervised methods and achieves state-of-the-art performance on this benchmark.