Paper Info
Title
A Novel Online Event Analysis Framework for Micro-blog Based on Incremental Topic Modeling
Abstract
In this paper, we present a scalable implementation of a topic modeling (Adaptive Link-IPLSA) based method for online event analysis, which summarize the gist of massive amount of changing tweets and enable users to explore the temporal trends in topics. This model also can simultaneously maintain the continuity of the latent semantics to better capture the time line development of events. With the help of this model, users can quickly grasp major topics in these twitters. The preliminary results show that our method leads to more balanced and comprehensive improvement for online event detection compared to benchmark approaches. Additionally our algorithm is computationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dynamics of microblogs.
Year
DOI
Venue
2012
10.1109/SNPD.2012.48
SNPD
Keywords
Field
DocType
near real-time scenario,attractive alternative,online event detection,adaptive link-iplsa,latent semantics,benchmark approach,online event analysis,comprehensive improvement,novel online event analysis,incremental topic modeling,massive amount,major topic,semantics,real time systems,topic model,computational modeling,data models,probability,algorithm design and analysis,micro blog
Data modeling,Data mining,Computer science,Artificial intelligence,Event analysis,Algorithm design,GRASP,Social media,Microblogging,Topic model,Semantics,Machine learning,Scalability
Conference
Citations 
PageRank 
References 
4
0.42
7
Authors
3
Name
Order
Citations
PageRank
Huifang Ma129029.69
Bo Wang29428.27
Ning Li33617.53