| DC Field | Value | Language |
|---|
| dc.contributor.author | Rybchak, Z. | - |
| dc.contributor.author | Basystiuk, O. | - |
| dc.date.accessioned | 2018-02-12T13:13:53Z | - |
| dc.date.available | 2018-02-12T13:13:53Z | - |
| dc.date.issued | 2017 | - |
| dc.identifier.citation | Rybchak Z. Analysis of methods and means of text mining / Z. Rybchak, O. Basystiuk // Econtechmod : an international quarterly journal on economics in technology, new technologies and modelling processes. – Lublin ; Rzeszow, 2017. – Volum 6, number 2. – P. 73–78. – Bibliography: 20 titles. | uk_UA |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/39423 | - |
| dc.description.abstract | In Big Data era when data volume doubled every year analyzing of all this data become really complicated task, so in this case text mining systems, techniques and tools become main instrument of analyzing tones and tones of information, selecting that information that suit the best for your needs and just help save your time for more interesting thing. The main aims of this article are explain basic principles of this field and overview some interesting technologies that nowadays are widely used in text mining. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | Commission of Motorization and Energetics in Agriculture | uk_UA |
| dc.subject | text mining | uk_UA |
| dc.subject | text analytics | uk_UA |
| dc.subject | data analysing | uk_UA |
| dc.subject | high-quality information | uk_UA |
| dc.subject | text categorization | uk_UA |
| dc.subject | text clustering | uk_UA |
| dc.subject | document summarization | uk_UA |
| dc.subject | sentiment analysis | uk_UA |
| dc.title | Analysis of methods and means of text mining | uk_UA |
| dc.type | Article | uk_UA |
| dc.contributor.affiliation | Lviv Polytechnic National University | uk_UA |
| dc.coverage.country | PL | uk_UA |
| dc.format.pages | 73-78 | - |
| dc.relation.referencesen | 1. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang. 2015. Fundamentals of Predictive Text Mining. Springer London Publishing House, 239 p. 2. Emma Tonkin, Gregory J. L. Tourte, Stephanie Taylor. 2016. Working with Text: Tools, Techniques and Approaches for Text Mining. Elsevier Science & Technology, 344 p. 3. Sonali Vijay, Archana Chaugule. Pramod Patil. 2014. Text Mining Methods and Techniques. International Journal of Computer Applications (0975–8887), Vol. 85, No. 17, р. 34. 4. Rebecca Merrett. 2015. 5 tools and techniques for text analytics. Available online at: http://www.cio.com. au/article/575209/5-tools-techniques-text-analytics/ 5. Daniel Gutierrez. 2015. Text Analytics: The Next Generation of Big Data. Available online at: http://www. predictiveanalyticsworld.com/patimes/text-analyticsthe- next-generation-of-big-data-061215/5529/ 6. ChengXiang Zhai, Sean Massung. 2016. Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining. Morgan & Claypool, 530 p. 7. Vidhya. K. A, G. Aghila. 2010. Text Mining Process, Techniques and Tools: an Overview. International Journal of Information Technology and Knowledge Management July-December 2010, Vol. 2, No. 2, pp. 613–622. 8. Markus Hofmann, Andrew Chisholm. 2016. Text Mining, Web Mining, and Visualization Use Cases Using Open Source Tools. Taylor & Francis, 500 p. 9. Ashish Kumar, Avinash Paul. 2016. Mastering Text Mining with R. Packt Publishing, Limited, 258 p. 10. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred Damerau. 2010. Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer Science & Business Media, 237 p. 11. Ronen Feldman, James Sange. 2013. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 410 p. 12. Tom Reamy. 2016. Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value from Social Media, and Add Bigger Text to Big Data. Today Inc, 415 p. 13. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang. 2015. Fundamentals of Predictive Text Mining. Springer London Publishing House, 239 p. 14. Andreas Hotho, Andreas Nurnberger. Gerhard Paaß, 2015. A Brief Survey of Text Mining. 15. Anne Kao, Steve R. Poteet. 2009. Natural Language Processing and Text Mining. Springer London Publishing House, 265 p. 16. Danny Sullivan. What Is Search Engine Spam? The Video Edition. Available online at: http://searchengineland.com/what-is-search-enginespam- the-video-edition-15202 17. Rasim M. Alguliev, Ramiz M. Aliguliyev, and Saadat A. Nazirova. 2011. Classification of Textual E-Mail Spam Using Data Mining Techniques. Applied Computational Intelligence and Soft Computing, Vol. 2011, 8 p. 18. Jiexun Li, Harry Jiannan Wang, Zhu Zhang and J. Leon Zhao. 2010. “A Policy-based Process Mining Framework: Mining Business Policy Texts for Discovering Process Models”’ Decision Support Systems table of contents archive, Vol. 48, Issue 1, рр. 267–288. 19. Ming Zhao, Jianli Wang and Guanjun Fan. 2008. “Research on Application of Improved Text Cluster Algorithm in Intelligent QA System”, Proceedings of the Second International Conference on Genetic and Evolutionary Computing, IEEE Computer Society, рр. 463–466. 20. Ulianovskaya, Yu. 2016. Information technology for treatment of results expert estimation with fuzzy character input data. ECONTECHMOD. An International Quarterly Journal, Vol. 5, No. 3, рр. 55–60. | uk_UA |
| dc.citation.journalTitle | Econtechmod | - |
| dc.citation.volume | Volum 6, number 2 | - |
| dc.coverage.placename | Lublin ; Rzeszow | uk_UA |
| Appears in Collections: | Econtechmod. – 2017. – Vol. 6, No. 2
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