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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Blei, D.: Introduction to Probabilistic Topic Models. Communications of the ACM (2011)
Steyvers, M., Griffiths, T.: Probabilistic Topic Models. Handbook of Latent Semantic Analysis 427 (2007)
Blei, D., Carin, L., Dunson, D.: Topic Models. IEEE Signal Processing Magazine 27(6), 55–65 (2010)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., Steyvers, M.: Learning Author-Topic Models from Text Corpora. ACM Transactions on Information Systems (TOIS) 28(1), 1–38 (2010)
Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The Author-Topic Model for Authors and Documents, pp. 478–494. AUAI Press (2004)
Shiozaki, H., Eguchi, K., Ohkawa, T.: Entity Network Prediction Using Multitype Topic Models. Springer (2008)
Newman, D., Chemudugunta, C., Smyth, P.: Statistical Entity-Topic Models, pp. 680–686 (2006)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)
AlSumait, L., Barbará, D., Gentle, J., Domeniconi, C.: Topic Significance Ranking of LDA Generative Models. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 67–82. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, Dm., Zheng, H., Yan, Jk., Jin, Y. (2012). Semantic Social Network Analysis with Text Corpora. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-30217-6_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30216-9
Online ISBN: 978-3-642-30217-6
eBook Packages: Computer ScienceComputer Science (R0)