PL EN


2016 | 296 | 64-85
Article title

The method of a two-level text-meaning similarity approximation of the customers’ opinions

Content
Title variants
PL
Metoda dwupoziomowego przybliżonego obliczenia podobieństwa znaczenia tekstów opinii klientów
Languages of publication
EN
Abstracts
EN
The method of two-level text-meaning similarity approximation, consisting in the implementation of the classification of the stages of text opinions of customers and identifying their rank quality level was developed. Proposed and proved the significance of major hypotheses, put as the basis of the developed methodology, notably about the significance of suggestions about the existence of analogies between mathematical bases of the theory of Latent Semantic Analysis, based on the analysis of semantic relationship between the variables and degree of participation of the document or term in the corresponding concept of the document data, and instruments of the theory of Social Network Analysis, directed at revealing the features of objects on the basis of information about structure and strength of their interaction. The Contextual Cluster Structure, as well as Quantitative Ranking evaluation for interpreting the quality level of estimated customers’ opinion has formed.
PL
Opracowano metodę dwupoziomowej aproksymacji podobieństwa – metodę przetwarzania tekstu, którą zastosowano w problemie klasyfikacji oraz do określania poziomu jakości klientów. Posługując się zaproponowaną w artykule metodyką, udowodniono istotność głównych hipotez, w szczególności hipotezy o istnieniu analogii pomiędzy podstawami matematycznymi LSA (ang. Latent Semantic Analysis), bazującej na analizie relacji semantycznej związku między stopniem udziału analogicznych pojęć w zbiorze dokumentów a narzędziami teorii analizy sieci społecznych (ang. Social Network Analysis), która z kolei odsłaniania cechy obiektów na podstawie informacji na temat struktury ich wzajemnych powiązań. Z połączenia powyższych metod wyłoniła się struktura klastra kontekstu, dająca ocenę ilościową na potrzeby ranking poziomu jakości opinii szacowanych klientów.
Year
Volume
296
Pages
64-85
Physical description
Contributors
author
  • Gdansk University of Technology. Faculty of Management and Economics. Department of Applied Informatics in Management
  • Gdansk University of Technology. Faculty of Management and Economics. Department of Applied Informatics in Management
  • Alfred Nobel University, Dnipropetrovs’k. Department of Applied Linguistics and Methods of Teaching Foreign Languages
References
  • S. Deerwester, S.T. Dumais, G.W. Furnas, Th.K. Landauer, R. Harshman, Indexing by Latent Semantic Analysis, “Journal of the American Society for Information Science” 1990, No. 41(6), pp. 391-407.
  • J.I. Maletic, N. Valluri, Automatic Software Clustering via Latent Semantic Analysis, 14th IEEE ASE’99, Cocoa Beach, FL October 12-15th 1999, pp. 251-254.
  • J.R. Paulsen, H. Ramampiaro, Combining Latent Semantic Indexing and Clustering to Retrieve and Cluster Biomedical Information: A 2-Step Approach. NIK-2009 conference, Proceedings of a Meeting Held 23-25 November 2009, Trondheim, Norway.
  • L. Jing, M.K. Ng, X. Yang, J.Z. Huang, A Text Clustering System Based on k-Means Type Subspace Clustering and Ontology. “International Journal of Intelligent Technology” 2006, Vol. 1(2), pp. 91-103.
  • D. Roussinov, J. Leon Zhao, Text Clustering and Summary Techniques for CRM Message Management, https://personal.cis.strath.ac.uk/dmitri.roussinov/Lim-Paper.pdf (accessed: 12.06.2016).
  • R. Řehůřek, Subspace Tracking for Latent Semantic Analysis [in:] European Conference on Information Retrieval, Springer, Berlin-Heidelberg 2011, pp. 289-300.
  • T. Pedersen, Duluth: Word Sense Induction Applied to Web Page Clustering, Proceedings of the 7th International Workshop Semantic Evaluation (SemEval 2013), in conjunction with the Second Joint Conference on Lexical and Computational Semantics (*SEM-2013), 2013, pp. 202-206.
  • D. Jurgens, The S-Space Package: An Open Source Package for Word Space Models, Proceedings ACLDemos ’10, Proceedings of the ACL System Demonstrations, 2010, pp. 30-35.
  • R. Řehůřek, P. Sojka, Software Framework for Topic Modelling with Large Corpora, Proceedings of the LREC 2010 Workshop, New Challenges for NLP Frameworks, 2010, pp. 45-50.
  • T. Hofmann, Probabilistic Latent Semantic Indexing, Proceedings of The 22nd Annual International SIGIR Conference Research and Development in Information Retrieval, Berkeley 1999, pp. 50-57.
  • R.B. Bradford, An Empirical Study of Required Dimensionality for large-Scale Latent Semantic Indexing Applications, Proceedings of the 17th ACM Conference on Information and Knowledge Management, Santa Cruz, CA 2008, pp. 153-162.
  • R.K. Ahuja, Th.L. Magnanti, J.B. Orlin, Network Flows: Theory, Algorithms, and Applications, Prentice Hall, Englewood Cliffs, NJ 1993.
  • B. Bollobas, Modern Graph Theory, Springer, Berlin-Heidelberg 1998.
  • D. West, Introduction to Graph Theory, Prentice Hall, Upper Saddle River, NJ 1996.
  • L.C. Freeman, Centrality in Social Networks: Conceptual Clarification, “Social Networks” 1979, Vol. 1 (3), pp. 223-258.
  • L.C. Freeman, Visualizing Social Networks, “Journal of Social Structure” 2000, Vol. 1(1).
  • L.C. Freeman, The Development of Social Network Analysis: A Study in the Sociology of Science, Empirical Press, Vancouver 2004.
  • A. Thomo, Latent Semantic Analysis, http://www.engr.uvic.ca/~seng474/svd.pdf (accessed: 1.06.2016).
  • M.E.J. Newman, M. Girvan, Finding and Evaluating Community Structure in Networks, “Physical Review” 2004, E. 69, 026113.
  • M.E.J. Newman, C. Moore, Finding Community Structure in Very Large Networks, “Physical Review” 2004, E 70, 066111.
  • P. Kapłanski, N. Rizun, Y. Taranenko, A. Seganti, Text-Mining Similarity Approximation Operators for Opinion Mining in BI tools, Chapter: Proceeding of the 11th Scientific Conference “Internet in the Information Society 2016”, Publisher of University of Dąbrowa Górnicza, Dąbrowa Górnicza, pp. 121-141.
  • N. Rizun, P. Kapłanski, Y. Taranenko, Development and Research of the Text Messages Semantic Clustering Methodology, Third European Network Intelligence Conference, ENIC, Wroclaw 2016.
  • P. Kapłanski, N. Rizun, Y. Taranenko, A. Seganti, Text-Mining Similarity Approximation Operators for Opinion Mining in BI tools [in:] M. Rostancki, P. Pikiewicz, K. Mączka, P. Buchwald, Proceeding of the 11th Scientific Conference “Internet in the Information Society-2016”, University of Dąbrowa Górnicza, Dąbrowa Górnicza 2016, pp.121-141.
  • P. Kapłanski, P. Weichbroth, Cognitum Ontorion: Knowledge Representation and Reasoning System [in:] Position Papers of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Łódz, Poland, September 13-16, 2015, http://dx.doi.org/10.15439/2015F17 (access: 28.05.2016).
  • P. Kapłanski, Controlled English Interface for Knowledge Bases, “Studia Informatica” 2011, Vol. 32, No. 2A, pp. 485-494.
  • A. Wroblewska, P. Kaplanski, P. Zarzycki, I. Lugowska, Semantic Rules Representation in Controlled Natural Language in FluentEditor [in:] Human System Interaction (HSI), The 6th International Conference on IEEE, 2013, pp. 90-96.
Document Type
Publication order reference
Identifiers
ISSN
2083-8611
YADDA identifier
bwmeta1.element.cejsh-0ffe0885-754a-4de4-99b3-deb164a19a84
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