Abstract
The hierarchical Dirichlet process model has been successfully used for extracting the topical or semantic content of documents and other kinds of sparse count data. Along with the growth of social media, there have been simultaneous increases in the amounts of textual information and social structural information. To incorporate the information contained in these structures, in this paper, we propose a novel non-parametric model, social hierarchical Dirichlet process (sHDP), to solve the problem. We assume that the topic distributions of documents are similar to each other if their authors have relations in social networks. The proposed method is extended from the hierarchical Dirichlet process model. We evaluate the utility of our method by applying it to three data sets: papers from NIPS proceedings, a subset of articles from Cora, and microblogs with social network. Experimental results demonstrate that the proposed method can achieve better performance than state-of-the-art methods in all three data sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Sina Weibo is one of the most popular websites providing microblogging services in China. http://www.weibo.com.
- 2.
- 3.
- 4.
The toolkit was downloaded from the website of the authors. https://www.cs.princeton.edu/~blei/topicmodeling.html.
References
Aldous, D.J.: Exchangeability and related topics. In: Hennequin, P.L. (ed.) École d’Été de Probabilités de Saint-Flour XIII—983. LNM, vol. 1117, pp. 1–198. Springer, Heidelberg (1985). https://doi.org/10.1007/BFb0099421
Blackwell, D., MacQueen, J.B.: Ferguson distributions via pólya urn schemes. Ann. Stat. 1, 353–355 (1973)
Blei, D.M., Frazier, P.I.: Distance dependent Chinese restaurant processes. J. Mach. Learn. Res. 12, 2461–2488 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chang, J., Blei, D.M.: Relational topic models for document networks. In: International Conference on Artificial Intelligence and Statistics, pp. 81–88 (2009)
Cowans, P.J.: Information retrieval using hierarchical Dirichlet processes. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 564–565. ACM (2004)
Fox, E.B., Sudderth, E.B., Jordan, M.I., Willsky, A.S.: An HDP-HMM for systems with state persistence. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 312–319. ACM, New York (2008). http://doi.acm.org/10.1145/1390156.1390196
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)
Kim, S., Smyth, P.: Hierarchical Dirichlet processes with random effects. In: NIPS, pp. 697–704 (2006)
Liu, Y., Niculescu-Mizil, A., Gryc, W.: Topic-link LDA: joint models of topic and author community. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 665–672. ACM, New York (2009). http://doi.acm.org/10.1145/1553374.1553460
McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and role discovery in social networks. Computer Science Department Faculty Publication Series, p. 3 (2005)
McCallum, A., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retr. J. 3, 127–163 (2000). www.research.whizbang.com/data
Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: Proceedings of the 17th International Conference on World Wide Web, pp. 101–110. ACM (2008)
Ren, L., Dunson, D.B., Carin, L.: The dynamic hierarchical Dirichlet process. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 824–831. ACM, New York (2008). http://doi.acm.org/10.1145/1390156.1390260
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 370–377. IEEE (2005)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 807–816. ACM, New York (2009). http://doi.acm.org/10.1145/1557019.1557108
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101(476), 1566–1581 (2006)
Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM (2006)
Zhang, J., Song, Y., Zhang, C., Liu, S.: Evolutionary hierarchical Dirichlet processes for multiple correlated time-varying corpora. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 1079–1088. ACM, New York (2010). http://doi.acm.org/10.1145/1835804.1835940
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Qian, J., Gong, Y., Zhang, Q., Huang, X. (2018). Hierarchical Dirichlet Processes with Social Influence. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-73618-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73617-4
Online ISBN: 978-3-319-73618-1
eBook Packages: Computer ScienceComputer Science (R0)