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首页> 外文会议>IEEE International Conference on Intelligence and Security Informatics >Topic evolution modeling in social media short texts based on recurrent semantic dependent CRP
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Topic evolution modeling in social media short texts based on recurrent semantic dependent CRP

机译：基于递归语义依赖的CRP的社交媒体短文主题演化建模

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摘要

Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.
机译：社交媒体已成为人们表达意见，共享信息和与他人交流的重要平台。从社交媒体检测和跟踪主题可以帮助人们掌握基本信息并促进许多与安全性相关的应用程序。由于社交媒体文本通常很短，因此基于LDA或HDP构建的传统主题演化模型通常会遭受数据稀疏性问题的困扰。最近提出的主题演化模型更适合于短文本，但是它们需要手动指定在不同时间段固定的主题编号。为了解决这些问题，本文提出了一种针对社交媒体短文本的非参数主题演化模型。我们首先提出循环语义依赖的中餐厅过程（rsdCRP），这是一个非参数过程，结合了词嵌入来捕获语义相似性信息。然后，我们将rsdCRP与单词共现建模相结合，并构建了面向短文本的主题演化模型sdTEM。我们对Twitter数据集进行实验研究。结果表明，与基准方法相比，我们的方法可有效监控社交媒体主题的演变。

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