微博动态主题挖掘融合了用户行为和时间窗口

Fei Wu, Zhuo Wang, Zhengtao Yu, Liren Wang, Feng Zhou
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引用次数: 1

摘要

与传统文本相比,微博文本具有用户行为特征和时间窗口特征。针对微博文本的特点,提出了一种融合用户行为和时间窗口的微博动态主题挖掘方法。在传统LDA模型的基础上,采用时间窗划分的方法将微博文本划分为各个时间窗,然后融合模型中的用户行为特征作为引导信息,依次构建融合用户行为和时间窗的微博动态主题挖掘。实验结果表明,我们提出的模型对微博话题分析和话题强度随时间变化有较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic topic mining for microblog fused with user's behavior and time window
Compared with traditional text, microblog text has features of user behavior and time window. Catered to features of microblog text, this paper proposed a method of dynamic topic mining for Microblog fused with user behavior and time window. Based on traditional LDA model, we use method of time window division to divide microblog text into each time window, then fuse features of user behavior in our model as guide information, sequentially construct dynamic topic mining for Microblog fused with user behavior and time window. Result of the experiment shows that the model we proposed has better effect on topic in microblog analyzing and topic intensity changing with time.
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