Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Relevance of Named Entities in Authorship Attribution Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Additive Regularization for Topic Modeling in Sociological Studies of User-Generated Texts

Authors : Murat Apishev, Sergei Koltcov, Olessia Koltsova, Sergey Nikolenko, Konstantin Vorontsov

Published in: Advances in Computational Intelligence

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Social studies of the Internet have adopted large-scale text mining for unsupervised discovery of topics related to specific subjects. A recently developed approach to topic modeling, additive regularization of topic models (ARTM), provides fast inference and more control over the topics with a wide variety of possible regularizers than developing LDA extensions. We apply ARTM to mining ethnic-related content from Russian-language blogosphere, introduce a new combined regularizer, and compare models derived from ARTM with LDA. We show with human evaluations that ARTM is better for mining topics on specific subjects, finding more relevant topics of higher or comparable quality. We also include a detailed analysis of how to tune regularization coefficients in ARTM models.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets
next chapter Effects of the Inclusion of Non-newsworthy Messages in Credibility Assessment
Literature
1.
Andrzejewski, D., Zhu, X.: Latent Dirichlet allocation with topic-in-set knowledge. In: Proceedings of NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing, SemiSupLearn 2009, pp. 43–48. Association for Computational Linguistics, Stroudsburg (2009)
2.
Andrzejewski, D., Zhu, X., Craven, M.: Incorporating domain knowledge into topic modeling via Dirichlet forest priors. In: Proceedings of 26th Annual International Conference on Machine Learning, ICML 2009, pp. 25–32. ACM, New York (2009)
3.
Apishev, M., Koltcov, S., Koltsova, O., Nikolenko, S., Vorontsov, K.: Mining ethnic content online with additively regularized topic models. Computacion y Sistemas 20(3), 387–403 (2016)
4.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)MATH
5.
Bodrunova, S., Koltsov, S., Koltsova, O., Nikolenko, S., Shimorina, A.: Interval semi-supervised LDA: classifying needles in a haystack. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013. LNCS (LNAI), vol. 8265, pp. 265–274. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-45114-0_​21 CrossRef
6.
Chemudugunta, C., Smyth, P., Steyvers, M.: Modeling general and specific aspects of documents with a probabilistic topic model. In: Advances in Neural Information Processing Systems, vol. 19, pp. 241–248. MIT Press (2007)
7.
Griffiths, T., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(Suppl. 1), 5228–5335 (2004)CrossRef
8.
9.
Jagarlamudi, J., Daumé III., H., Udupa, R.: Incorporating lexical priors into topic models. In: Proceedings of EACL 2012, pp. 204–213 (2012)
10.
Koltcov, S., Koltsova, O., Nikolenko, S.I.: Latent Dirichlet allocation: stability and applications to studies of user-generated content. In: Proceedings of WebSci 2014, pp. 161–165 (2014)
11.
Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of EMNLP 2011, pp. 262–272 (2011)
12.
Nikolenko, S.I., Koltsova, O., Koltsov, S.: Topic modelling for qualitative studies. J. Inf. Sci. 43, 88–102 (2015)CrossRef
13.
Paul, M.J., Dredze, M.: Discovering health topics in social media using topic models. PLoS ONE 9(8), e103408 (2014)CrossRef
14.
Sociopolitical processes in the internet. Laboratory for Internet Studies. Internal report, National Research University Higher School of Economics, reg. no. 01201362573, Moscow (2013)
15.
Tan, Y., Ou, Z.: Topic-weak-correlated latent Dirichlet allocation. In: 7th International Symposium Chinese Spoken Language Processing (ISCSLP), pp. 224–228 (2010)
16.
Tikhonov, A.N., Arsenin, V.Y.: Solution of Ill-Posed Problems. W.H. Winston, Washington, D.C. (1977)MATH
17.
Vorontsov, K.V., Potapenko, A.A.: Tutorial on probabilistic topic modeling: additive regularization for stochastic matrix factorization. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorskiy, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 29–46. Springer, Cham (2014). doi:10.​1007/​978-3-319-12580-0_​3
18.
Vorontsov, K.V., Potapenko, A.A.: Additive regularization of topic models. Mach. Learn. 101(1), 303–323 (2015). Special Issue on Data Analysis and Intelligent Optimization with ApplicationsMathSciNetCrossRefMATH
19.
Vorontsov, K., Frei, O., Apishev, M., Romov, P., Suvorova, M., Yanina, A.: Non-bayesian additive regularization for multimodal topic modeling of large collections. In: Proceedings of TM 2015, pp. 29–37. ACM, New York (2015)
20.
Metadata
Title
Additive Regularization for Topic Modeling in Sociological Studies of User-Generated Texts
Authors
Murat Apishev
Sergei Koltcov
Olessia Koltsova
Sergey Nikolenko
Konstantin Vorontsov
Copyright Year
2017
Book
Advances in Computational Intelligence

Print ISBN: 978-3-319-62433-4

Electronic ISBN: 978-3-319-62434-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-62434-1_14

Premium Partner

    Image Credits