Dynamic Semantic Network Analysis of Unstructured Text Corpora

  • Alexander KharlamovEmail author
  • Galina Gradoselskaya
  • Sofia Dokuka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10716)


The natural language structure can be viewed as weighted semantic network. Such representation gives an option to investigate the text corpus as the model of the subject domain. In this paper we propose the mechanism of the semantic network identification and construction. We apply the methodological instrument for the social media text analysis and trace the dynamics of the discussions about 1917 year within the internet communities. Network changes illustrate the changes of the interest to different topics. The proposed mechanism can be used for the monitoring of the different social processes and phenomenal in online social networks and media.


Text mining Unstructured text processing Neural networks N-gram text model Semantic networks Thematic trees Structural analysis 1917 


  1. 1.
    Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRefGoogle Scholar
  2. 2.
    Dumais, S.T., Furnas, G.W., Landauer, T.K., Deerwester, S.: Using latent semantic analysis to improve information retrieval. In: Conference on Human Factors in Computing, pp. 281–285. ACM, New York (1988)Google Scholar
  3. 3.
    Hofmann, T.: Probabilistic latent semantic analysis. In: UAI 1999, pp. 289–296. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  4. 4.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Kharlamov, A.: Assotsiativnaya pamyat’ - sreda dlya formirovaniya prostranstva znaniy. Palmarium Academic Publishing, Dusseldorf (2017). (in Russian)Google Scholar
  6. 6.
    Kharlamov, A.A., Yermolenko, T.V., Zhonin, A.A.: Modeling of process dynamics by sequence of homogenous semantic networks on the base of text corpus sequence analysis. In: Ronzhin, A., Potapova, R., Delic, V. (eds.) SPECOM 2014. LNCS (LNAI), vol. 8773, pp. 300–307. Springer, Cham (2014). Google Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute of Higher Nervous Activity and Neurophysiology of RASMoscowRussia
  2. 2.Moscow State Linguistic UniversityMoscowRussia
  3. 3.Higher School of EconomicsNational Research UniversityMoscowRussia

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