Abstract
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.
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- Category:
- Articles
- Type:
- artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
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Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach
no. 296,
pages 64 - 85,
ISSN: 2083-8611 - Publication year:
- 2016
- Bibliographic description:
- Rizun N., Kapłański P., Yurii T.: The Method of a Two-Level Text-Meaning Similarity Approximation of the Customers’ Opinions// Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach. -Vol. 296., (2016), s.64-85
- Bibliography: test
-
- Deerwester, Scott, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, Richard Harshman. Indexing by Latent Semantic Analysis. 1990, № 41(6), pp. 391-407. open in new tab
- Jonathan I. Maletic, Naveen Valluri. Automatic Software Clustering via Latent Semantic Analysis. 14th IEEE ASE'99, Cocoa Beach FL, Oct. 12-15th, pp. 251-254 open in new tab
- Jon Rune Paulsen, Ramampiaro H. Combining Latent Semantic Indexing and Clustering to Retrieve and Cluster Biomedical Information: A 2-step Approach. NIK-2009 conference.
- Jing L., Ng M. K., Yang X., Huang J. Z. A text clustering system based on k-means type subspace clustering and ontology. International Journal of Intelligent Technology, 1(2): 91-103, 2006. open in new tab
- Roussinov D., Leon Zhao J. Text Clustering and Summary Techniques for CRM Message Management. [Online]. Available: https://personal.cis.strath.ac.uk/dmitri.roussinov/Lim- Paper.pdf open in new tab
- Řehůřek, R. Subspace tracking for latent semantic analysis. Advances in Information Retrieval, 2011, pp. 289-300. open in new tab
- Pedersen, T. Duluth: Word Sense Induction Applied to Web Page Clustering : Proceedings of the 7th inter. workshop Semantic Evaluation (SemEval 2013), in conjunction with the Second Joint Conference on Lexical and Computational Semantics (*SEM-2013), 2013, pp. 202-206. open in new tab
- Jurgens D. The S-Space Package: An Open Source Package for Word Space Models. open in new tab
- Proceedings ACLDemos '10. Proceedings of the ACL System Demonstrations, 2010, pp. 30-35. open in new tab
- Řehůřek R., Sojka. Software Framework for Topic Modelling with Large Corpora. Proceedings of the LREC 2010 workshop. New Challenges for NLP Frameworks, 2010, pp. 45- 50.
- Hofmann T. Probabilistic Latent Semantic Indexing. Proceedings of the twenty-second annual inter. SIGIR conf. Research and Development in Information Retrieval, 1999, pp. 50-57. open in new tab
- Roger B. Bradford. An empirical study of required dimensionality for large-scale latent semantic indexing applications. Proceedings of the 17th ACM conf. / B. Roger Bradford // Information and Knowledge Management, 2008. -pp. 153-162. open in new tab
- Ahuja R. K., Magnanti Thomas L., Orlin, J. B. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, Englewood Cliffs, NJ. 1993. open in new tab
- Bollobas B. Modern Graph Theory. Springer, 1998. open in new tab
- West, D. Introduction to Graph Theory. Prentice Hall. 1996 open in new tab
- Freeman L. C. Centrality in social networks: Conceptual clarification. Social Networks, 1979, 1 (3), pp. 223-258. open in new tab
- Freeman L. C. Visualizing social networks. Journal of Social Structure, 2000, 1 (1). open in new tab
- Freeman L. C. The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical Press. 2004
- M.E.J. Newman, M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E. 69: 026113, 2004. open in new tab
- M.E.J. Newman, C. Moore. Finding community structure in very large networks. Phys. Rev. E 70, 066111, 2004.
- Kapłanski P., Rizun N., Taranenko Y., Seganti A. Text-mining Similarity Approximation Operators for Opinion Mining in BI tools. Chapter: Proceeding of the 11th Scientific Congerence "Internet in the Information Society-2016", Publisher: University of Dąbrowa Górnicza, pp.121-141.
- Rizun N., Kapłanski P., Taranenko Y. Development and Research of the Text Messages Semantic Clustering Methodology. 2016, Third European Network Intelligence Conference, Publisher: ENIC, 2016.33, pp.180-187 open in new tab
- Kapłanski P., Weichbroth P., Cognitum Ontorion: Knowledge Representation and Reasoning System, in Position Papers of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, September 13-16, 2015., 2015. doi: 10.15439/2015F17 pp. 177-184. [Online]. Available: http://dx.doi.org/10.15439/2015F17 open in new tab
- Kapłanski P. Controlled English interface for knowledge bases, Studia Informatica, vol. 32, no. 2A, pp. 485-494, 2011
- Wroblewska A., Kaplanski P., Zarzycki P., Lugowska I., Semantic Rules Representation in Controlled Natural Language in FluentEditor, in Human System Interaction (HSI), 2013 The 6th International Conference on. IEEE, 2013, pp. 90-96. open in new tab
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