Subject-Oriented Data Retrieval and Analysis on Sina Weibo

被引：0
|
作者：
Lo, Dan Chia-Tien [1 ]
Garnder, Charles [1 ]
Paschos, Pascal [2 ]
Ng, Chung [3 ]
机构：
[1] Kennesaw State Univ, Dept Comp Sci, Marietta, GA 30060 USA
[2] Chicago State Univ, Dept Comp Sci, Chicago, IL USA
[3] Morehouse Coll, Dept Comp Sci, Atlanta, GA USA
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2019年
关键词：
social media; mining; extraction; Weibo; analytics; SIMILARITY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The goal of this project is to timely identify and characterize hot topics discussed on the Chinese microblogging website, Sina Weibo. First, we develop a subject-oriented crawler to retrieve Weibo posts. Second, a probabilistic topic model is applied to the dataset to identify prominent topics and keywords. A comparison on hot topics, Word Cloud, and Latent Dirichlet Allocation results are discussed, followed by future research directions.
引用
收藏
页码：4431 / 4434
页数：4
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