Semi-Supervised Sentence Classification Based on User Polarity in the Social Scenarios

Abstract

The data sparsity is the main challenge in sentence classification in social scenarios, the recent methods incorporate user information by encoding user node in the user-relation network to alleviate this issue. However, the connection between users is not always available due to privacy protection or other commercial reasons. Thus, in this paper, a concept called user polarity is proposed to quantify the tendency of sentences published by a user which are categorized into the same class. Then a self-training framework based on user polarity is proposed, which incorporates user information without connection between users, to alleviate the data sparsity in sentence classification. A regularization term is used to strengthen the prediction of the model in some special points, and a sample selector is designed to reduce the noise in the pseudo-labeled data generated in self-training process. Besides, some hard samples are selected to improve the retraining process. The experimental results conducted on SemEval 2019 task 8 indicate that our method performs significantly better than other three semi-supervised methods and achieves state-of-the-art performance on this benchmark.