（Faculty of Information Engineering and Automation，Kunming University of Science and Technology，Kunming 650500，China）（Yunnan Key Laboratory of Artificial Intelligence，Kunming University of Science and Technology，Kunming 650500，China）
Abstract：The generation of case public opinion timeline is to generate topic clusters of public opinion news of the same case in chronological order，which is of great significance for users to understand the development process of the case.In essence，it can be regarded as an unsupervised clustering task under time constraints.However，the public opinion news describing the same case may have many similar elements that lead to overlapping representations in the clustering space.In order to generate more discriminative text representations，based on the auto-encoding framework，a method for generating public opinion timeline of cases with enhanced elements of different cases is proposed.First，a dataset of the public opinion involved in the case is constructed and the different elements of each Weibo text is generated;then the different elements，Weibo text and the time of the case are used as the input of the BERT encoder，and the low-dimensional feature vector of the text is generated based on the auto-encoding framework;Finally，based on the feature vector and K-Means clustering method，soft clustering is used to generate the public opinion timeline of the case.The experimental results show that on the constructed timeline dataset of public opinion involved in the case，the proposed method has a great improvement in the two clustering indicators of ACC and NMI.
高盛祥,赵瑶,余正涛,黄于欣. 差异性案件要素增强的案件舆情时间线生成方法[J]. 小型微型计算机系统, 2022, 43(9): 1902-1907.
GAO Sheng-xiang,ZHAO Yao,YU Zheng-tao,HUANG Yu-xin. Method of Generating Public Opinion Timeline of Cases with Enhanced Elements of Different Cases. Journal of Chinese Computer Systems, 2022, 43(9): 1902-1907.
2022, Vol. 43 
