Semantic social network analysis with text corpora

Authors:
Dong-mei Yang;Hui Zheng;Ji-kun Yan;Ye Jin
Affiliations:
Science and Technology on Blind Signal Processing Laboratory, China;Science and Technology on Blind Signal Processing Laboratory, China;Southwest Electronics and Telecommunication Technology Research Institute, China;Southwest Electronics and Telecommunication Technology Research Institute, China
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2012

Citing 7
Cited 0

Latent dirichlet allocation

The Journal of Machine Learning Research
The author-topic model for authors and documents

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Statistical entity-topic models

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Topic Significance Ranking of LDA Generative Models

ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Learning author-topic models from text corpora

ACM Transactions on Information Systems (TOIS)
Probabilistic topic models

Communications of the ACM

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present the Document-Entity-Topic (DET) model for semantic social network analysis which tries to find out the interested entities through the topics we aim at, detect groups according to the entities which concern the similar topics, and rank the plentiful entities in a document to figure out the most valuable ones. DET model learns the topic distributions by the literal descriptions of entities. The model is similar to Author-Topic (AT) model, adding the key attribute that the distribution of entities in a document is not uniform but Dirichlet allocation. We experiment on the "Libya Event" data set which is collected from the Internet. DET model increases the precision on tasks of social network analysis and gives much lower perplexity than AT model.