Abstract
One of the ways to describe the content of internet sources is known as topic modeling, which tries to uncover the hidden thematic structures in document collections. Topic modeling applied to social networks can be useful for analysis in case of crisis situations, elections, launching a new product on the market etc. It becomes popular research area in recent years and represents the methods to browse, search and summarize large amount of the textual data. The main aim of this paper is to describe a new way for topic modeling based on the usage of Formal Concept Analysis combined with reduction by Singular Value Decomposition of the input data matrix. In difference to other common used method for topic modeling our proposed method is able to generate topic hierarchy, which offer more detail analysis of topics within the collection. Our approach is experimentally tested on the selected dataset of Twitter network contributions.
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References
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 694–703 (2003)
Petterson, J., Buntine, W., Narayanamurthy, S., Caetano, T., Smola, A.: Word features for latent dirichlet allocation. Adv. Neural. Inform. Process. Syst. 23, 1921–1929 (2010)
Zhai, K., Boyd-Graber, J.: Online latent dirichlet allocation with infine vocabulary. In: Proceedings of ICML 2013, Atlanta, US, pp. 561–569 (2013)
Li, X., Ouyang, J., Lu, Y.: Topic modeling for large-scale text data. Front. Electr. Electron. Eng. 16(6), 457–465 (2015)
Hoffman, M., Blei, D., Wang, C., Paisley, D.: Stochastic variational inference. J. Mach. Learn. Res. 14, 1303–1347 (2013)
Blei, D., Griffiths, T., Jordan, M.: The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. J. ACM 57(2), article number 7, 1–30 (2010)
Hofmann, T.: The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data. In: Proceedings of IJCAI99, Stockholm, Sweden, pp. 682–687 (1999)
Paisley, J., Wang, C., Blei, D., Jordan, M.I.: Nested hierarchical dirichlet processes. IEEE Trans. Pattern Anal. Mach. Intell. 37(2), 256–270 (2015)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2012)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Berlin (1999)
Medina, J., Ojeda-Aciego, M., Ruiz-Calviño, J.: Formal concept analysis via multi-adjoint concept lattices. Fuzzy Set. Syst. 160, 130–144 (2009)
Antoni, L., Krajci, S., Kridlo, O., Macek, B., Piskova, L.: On heterogeneous formal contexts. Fuzzy Set. Syst. 234, 22–33 (2014)
Krajči, S.: A generalized concept lattice. Logic J. IGPL 13(5), 543–550 (2005)
Butka, P., Pócs, J.: Generalization of one-sided concept lattices. Comput. Inf. 32(2), 355–370 (2013)
Butka, P., Pocs, J.: Pocsova: On equivalence of conceptual scaling and generalized one-sided concept lattices. Inf. Sci. 259, 57–70 (2014)
Pocs, J., Pocsova, J.: Basic theorem as representation of heterogeneous concept lattices. Front. Comput. Sci. 9(4), 636–642 (2015)
Pocs, J., Pocsova, J.: Bipolarized extension of heterogeneous concept lattices. Appl. Math. Sci. 8(125–128), 6359–6365 (2014)
Antoni, L., Krajci, S., Kridlo, O.: Randomized Fuzzy Formal Contexts and Relevance of One-Sided Concepts, vol. 9113, pp. 183–199. ICFCA 2015, LNAI (Subseries of LNCS) (2014)
Butka, P., Pocs, J., Pocsova, J.: Reduction of concepts from generalized one-sided concept lattice based on subsets quality measure. Adv. Intell. Syst. Comput. 314, 101–111 (2015)
Kardos, F., Pocs, J., Pocsova, J.: On concept reduction based on some graph properties. Knowl. Base Syst. 93, 67–74 (2016)
Melo, C., Le-Grand, B., Aufaure, A.: Browsing large concept lattices through tree ex-traction and reduction methods. Int. J. Intell. Inf. Technol. (IJIIT) 9(4), 16–34 (2013)
Snasel, V., Polovincak, M., Abdulla, H.: Concept lattice reduction by singular value decomposition. In: Proceedings of the SYRCoDIS 2007, Moscow, Russia (2007)
Kumar, C.A., Srinivas, S.: Concept lattice reduction using fuzzy k-means clustering. Expert Syst. Appl. 37(3), 2696–2704 (2010)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Sarnovsky, M., Carnoka, N.: Distributed algorithm for text documents clustering based on k-means approach. Adva. Intell. Syst. Comput. 430, 165–174 (2016)
Sarnovsky, M., Ulbrik, Z.: Cloud-based clustering of text documents using the GHSOM algorithm on the GridGain platform. Proc. SACI 2013, 309–313 (2013)
Babic, F., Paralic, J., Bednar, P., Racek, M.: Analytical framework for mirroring and reflection of user activities in e-Learning environment. Adv. Intell. Soft Comput. 80, 287–296 (2010)
Paralic, J., Richter, C., Babic, F., Wagner, J., Racek, M.: Mirroring of knowledge practices based on user-defined patterns. J. Univers. Comput. Sci. 17(10), 1474–1491 (2011)
Acknowledgments
The work presented in this paper was supported by the Slovak VEGA grant 1/0493/16 and Slovak KEGA grant 025TUKE-4/2015.
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Smatana, M., Butka, P. (2017). Hierarchical Topic Modeling Based on the Combination of Formal Concept Analysis and Singular Value Decomposition. In: Zgrzywa, A., Choroś, K., Siemiński, A. (eds) Multimedia and Network Information Systems. Advances in Intelligent Systems and Computing, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-43982-2_31
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DOI: https://doi.org/10.1007/978-3-319-43982-2_31
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