面向社会事件的半监督自训练多方立场分析

doi:10.16451/j.cnki.issn1003-6059.201812002

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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.

Key words： Multiple Standpoint Analysis Semi-supervised Self-training User-Level Standpoint Consistency Topic Information

Received: 12 February 2018

ZTFLH:

TP 24

Fund:Supported by National Natural Science Foundation of China(No.71702181,11832001)

About author:: (LIN Junjie, Ph.D. candidate. His resear-ch interests include social media analysis and text mining.)
(WANG Lei(Corresponding author), Ph.D., associate professor. His research inte-rests include social media information proce-ssing.)
(MAO Wenji, Ph.D., professor. Her research interests include artificial intelligence and social computing.)

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	LIN Junjie
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Cite this article:

LIN Junjie,WANG Lei,MAO Wenji. Semi-supervised Self-training for Multiple Standpoint Analysis in Social Events[J]. , 2018, 31(12): 1074-1084.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201812002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I12/1074

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