Name
Abstract | ||
|---|---|---|
Detecting and tracking events from the text stream data is critical to social network society and thus attracts more and more research efforts. However, there exist two major limitations in the existing topic detection and tracking models, i.e. noise words and multiple sub-events. In this paper, a novel event detection and tracking algorithm, topic event detection and tracking (TEDT), was proposed to tackle these limitations by clustering the co-occurrent features of the underlying topics in the text stream data and then the evolution of events was analyzed for the event tracking purpose. The evaluation was performed on two real datasets with the promising results demonstrating that (1) the proposed TEDT algorithm is superior to the state-of-the-art topic model with respect to event detection; (2) the proposed TEDT algorithm can successfully track the event changes. © 2014 Springer International Publishing Switzerland. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-05476-6_5 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
event detection,Social media,temporal analysis,topic model | Data mining,Social media,Social network,Computer science,Complex event processing,Artificial intelligence,Topic model,Cluster analysis,Event tracking,Text stream,Machine learning | Conference |
Volume | Issue | ISSN |
8397 LNAI | PART 1 | 03029743 |
ISBN | Citations | PageRank |
978-3-319-05476-6; 978-3-319-05475-9 | 1 | 0.38 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Chunshan | 1 | 2 | 0.73 |
| Ye Yunming | 2 | 440 | 39.77 |
| Zhang Xiaofeng | 3 | 101 | 18.32 |
| Dianhui Chu | 4 | 46 | 11.43 |
| Deng Shengchun | 5 | 673 | 40.79 |
| Xiaofei Xu | 6 | 408 | 70.26 |