基于正文和评论交互注意的微博案件方面识别

Aspect identification of microblog cases based on the interactive attention of contents and comments

微博案件观点所涉方面的自动识别是了解互联网社交媒体新闻舆情的重要手段,但由于微博文本形式和内容均灵活多变,传统的方面识别方法通常只利用单一的正文或评论,使微博语义理解非常有限。针对涉案微博文本的方面识别问题开展研究,提出一种基于正文和评论交互注意的案件方面识别方法,通过融合社交媒体的上下文信息,实现对案件观点所涉方面的识别。首先基于Transformer框架对正文和评论分别进行编码;然后基于交互注意力机制,实现正文信息和评论信息的融合,并基于融合后的特征实现对评论文本案件方面的识别;最后基于12个案件构建的微博数据集进行实验,实验结果表明,采用交互注意力机制融合微博正文信息和评论信息可以显著提升案件方面识别的准确率,证明了所提方法的有效性。

The automatic identification of aspects involved in microblog cases is an important means to understand the public opinion of the Internet social media news.However,the text format and content of the microblog are flexible and changeable,and the traditional aspect identification methods usually use only a single text or comment,which brings great difficulties to the understanding of microblog semantics.This paper studies the identification of aspects in the microblog text involved in the cases,proposes an aspect identification method of microblog cases based on the interactive attention of contents and comments,and realizes the aspect identification of microblog cases by integrating the contextual information of social media.The paper firstly encodes the contents and comments individually based on the Transformer framework,realizes the fusion of the content information and the comment information based on the interactive attention mechanism,and realizes the aspect identification of the comment text based on the fused features.Finally,experiments were conducted based on the microblog dataset containing 12 cases.The experimental results show that using the interactive attention to fuse microblog content information can significantly improve the accuracy of aspect identification,which proves the effectiveness of the method proposed in the paper.