Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer
Xingyi Cheng, Weidi Xu, Taifeng Wang, Wei Chu, Weipeng Huang, Kunlong Chen, Junfeng Hu · 2019
Aspect-term sentiment analysis (ATSA) is a long-standing challenge in natural language processing.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 semisupervised method for the ATSA problem by using the Variational Autoencoder based on Transformer.The model learns 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 supervised models can be integrated.Experimental results are obtained on the SemEval 2014 task 4 and show that our method is effective with different five specific classifiers and outperforms these models by a significant margin.