Abstract: 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.

Key words: text processing for social media, microblog case, aspect identification, interactive attention, Transformer