Previous studies on cross-domain aspect-based sentiment classification depend on the pivot features or utilize the target data for representation learning, which ignores the correlations between instances and words. In this study, we employ two strategies to connect different domains through tripartite graphs. Firstly, we employ aword-topic-instance tripartite graphto bridge the gap between different domains with the cross-domain topic distribution. The cross-domain topic distribution is learned by a neural topic model on different domains. Secondly, we use aword-pivot-instance tripartite graphto connect instances and words in different domains. The pivot clauses are generated from the instances, and are domain-independent and sentimentally aligned with the original text. Afterward, we employ graph convolutional networks to model over these tripartite graphs for cross-domain sentiment classification respectively. The experimental result shows that our model with tripartite graphs outperforms several competitive models. The result also indicates the effectiveness of the proposed tripartite graphs for cross-domain aspect-based sentiment classification.

Sentiment classification aims to detect polarity from a piece of text. The polarity is usually positive or negative, and the text genre is usually product review. The challenges of sentiment classification are that it is hard to capture semantic of reviews, and the labeled data is hard to annotate. Therefore, we propose neural co-training to learn the semantic representation of each review using the neural network model, and learn the information from unlabeled data using a co-training framework. In particular, we use the attention-based bi-directional Gated Recurrent Unit (Att-BiGRU) to model the semantic content of each review and regard different categories of the target product as different views. We then use a co-training framework to learn and predict the unlabeled reviews with different views. Experiment results with the Yelp dataset demonstrate the effectiveness of our approach.