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Text-mining Similarity Approximation Operators for Opinion Mining in BI tools

Abstract

The concept of the Text-mining Similarity Approximation Operators for Opinion Mining as extensions to Natural Language Interface Database is defined. The new operators: “keywords of” dimension; subsetting operator “about C is q”; aggregation operator “by similar C” are proposed. These operators are based on the Latent Semantic Analysis and Social Network Analysis

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Category:
Conference activity
Type:
materiały konferencyjne indeksowane w Web of Science
Title of issue:
Proceeding of the 11th Scientific Congerence "Internet in the Information Society-2016" strony 121 - 141
Language:
English
Publication year:
2016
Bibliographic description:
Rizun N., Kapłański P., Yurii T., Alessandro S..: Text-mining Similarity Approximation Operators for Opinion Mining in BI tools, W: Proceeding of the 11th Scientific Congerence "Internet in the Information Society-2016", 2016, University of Dąbrowa Górnicza,.
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  1. Andreev A., Berezkin D., Morozov V., Simakov K.. "The method of clustering texts collections and clusters annotating". Digital Libraries: Advanced Methods and Technologies, Digital Collections: Proceedings 10th Scientific Conference (RCDL'2008). -Dubna, 2008. pp. 220-229.
  2. Bollobas B., "Modern Graph Theory, ser. Graduate Texts in Mathematics". Springer New York, 1998. [Online]. open in new tab
  3. Bradford R.B., "An empirical study of required dimensionality for large-scale latent semantic indexing applications," in Proceedings of the 17th ACM Conference on Information and Knowledge Management, ser. CIKM '08. New York, NY, USA: ACM, 2008, pp. 153-162. [Online]. open in new tab
  4. Available: http://doi.acm.org/10.1145/1458082.1458105 open in new tab
  5. Clauset A., Newman M.E.J., and Moore C., "Finding community structure in very large networks," Physical Review E, pp. 1-6, 2004. [Online]. Available: www.ece.unm.edu/ifis/papers/community-moore.pdf open in new tab
  6. Dobrowolski D., Kaplanski P., Marciniak A., and Lojewski Z., "Semantic OLAP with FluentEditor and Ontorion Semantic Excel Toolchain," IARIA, vol. SEMAPRO 2015: The Ninth International Conference on Advances in Semantic Processing, 2015. [Online]. Available: https://www.thinkmind.org/index.php?view= article&articleid=semapro_2015_3_30_30051 open in new tab
  7. Dumis S, Fumas G, Landauer T et al. "Using Latent Semantic Analysis to Improve Access to Textual Information". Proceedings of Computer Human Interaction, 1988.217-285 open in new tab
  8. Hofmann T., "Probabilistic latent semantic indexing," in Proceedings of the 22Nd open in new tab
  9. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ser. SIGIR '99. New York, NY, USA: ACM, 1999, pp. 50-57. [Online]. Available: http://doi.acm.org/10.1145/312624.312649 open in new tab
  10. Jurgens D. and Stevens K., "The s-space package: An open source package for word space models," in Proceedings of the ACL 2010 System Demonstrations.
  11. Newman M. E. J. and Girvan M., "Finding and evaluating community structure in networks," Physical Review, vol. E 69, no. 026113, 2004. open in new tab
  12. Nolan C., "Manipulate and query olap data using adomd and multidi-mensional expressions." Microsoft Systems Journal, no. 63, pp. 51-59, 1999.
  13. Øehùøek R. and Sojka P., "Software framework for topic modeling with large corpora," in Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks. Valletta, Malta: University of Malta, 2010, pp. 46-50. [Online]. Available: http://www.fi.muni.cz/usr/sojka/presentations/lrec2010-poster-rehurek- sojka.pdf
  14. Pedersen T., "Duluth : Word sense induction applied to web page clustering," in Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013). Atlanta, Georgia, USA: Association for Computational Linguistics, June 2013, pp. 202-206. [Online]. Available: http://www.aclweb.org/anthology/S13- 2036 open in new tab
  15. Ranta A., Controlled Natural Language: 4th International Workshop, CNL 2014, Galway, Ireland, August 20-22, 2014. Proceedings. Cham: Springer International Publishing, 2014, ch. Embedded Controlled Languages, pp. 1-7. [Online]. Available: http://dx.doi.org/10.1007/ 978-3-319-10223-8_1 open in new tab
  16. Rehurek R., "Subspace tracking for latent semantic analysis," in Advances in Information Retrieval -33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings, 2011, pp. 289-300. [Online].
  17. Available: http://dx.doi.org/10.1007/ 978-3-642-20161-5_29 open in new tab
  18. Rizun N., Kapłanski P., Taranenko Y. "Development and Research of the Text Messages Semantic Clustering Methodology", The Third European Network Intelligence Conference (ENIC 2016), Proceedings, 2016. open in new tab
  19. Salton G, Wong A, Yang CS. "A Vector Space Model for Automatic Indexing". Communications of the ACM,1995,18(11): pp 613-620. open in new tab
  20. Seganti A., Kaplanski P., Campo J.D.N, Cieslinski K., J. Kozi-olkiewicz, and P.
  21. Zarzycki, "Asking data in a controlled way with Ask Data Anything NQL," vol. CNL2016 Conference. Springer, 2016.
  22. West D., Introduction to Graph Theory, ser. Featured Titles for Graph Theory Series. Prentice Hall, 2001. [Online]. open in new tab
  23. Xuren Wang, Qiuhui Zheng. "Text Emotion Classification Research Based on Improved Latent Semantic Analysis Algorithm". Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) open in new tab
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