Semi-supervised Self-training for Multiple Standpoint Analysis in Social Events
LIN Junjie1,2, WANG Lei1, MAO Wenji1,2
1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049
Abstract：Existing methods for standpoint analysis mainly train standpoint classification models in a supervised or unsupervised manner. It usually needs a large number of labeled data to support the training of supervised models. In contrast, the performance of unsupervised models differs greatly from that of the supervised models. To reduce the demand of labeled data in model training, and meanwhile to ensure model performance, this paper proposes a semi-supervised self-training method for multiple standpoint analysis based on social media texts related to social events. For self-training methods, selecting and adding high-quality data to the training dataset play a key role in improving the performance of classification models during the iterative training process. The proposed method first measures the classification confidence of texts based on user-level standpoint consistency. It then leverages topic information to select high-quality texts to expand the training dataset, so as to constantly improve the performance of the model. Experimental results show that the proposed method can achieve better performance in standpoint classification compared with the representative methods in the related work as well as other semi-supervised model training methods. In addition, both the user-level standpoint consistency and topic information used in the method contribute to improve the performance of standpoint classification.
林俊杰, 王磊, 毛文吉. 面向社会事件的半监督自训练多方立场分析[J]. 模式识别与人工智能, 2018, 31(12): 1074-1084.
LIN Junjie, WANG Lei, MAO Wenji. Semi-supervised Self-training for Multiple Standpoint Analysis in Social Events. , 2018, 31(12): 1074-1084.
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2018, Vol. 31 
