Author(s):
B Uday Jaswanth Redddy, CHA Raghu Rami Reddy, B Kavya
Email(s):
201910101259@presidencyuniversity.in , 201910101077@presidencyuniversity.in , 201910100996@presidencyuniversity.in
DOI:
10.52711/2321-581X.2023.00001
Address:
B Uday Jaswanth Redddy, CHA Raghu Rami Reddy, B Kavya
B. Tech, Presidency University, Kolkata, West Bengal 700073.
*Corresponding Author
Published In:
Volume - 14,
Issue - 1,
Year - 2023
ABSTRACT:
The Tremendous advancements in digital data collecting techniques have resulted in massive large datasets. unstructured data makes up well over 80% of today's data. The identification of wonderful ways and characteristics to learn text - based files from a massive amount of datasets are a major issue. Text mining /Data analytics is the process for identifying interesting and difficult problem patterns in vast quantities of text information. For mining textual material and uncovering useful facts for making predictions and decision making, there are various ways and methods available. Choosing an acceptable and wonderful text/word - based analysis approach improves speed and saves both time required to obtain useful data from huge amount of unstructured data. This research will highlight & study all primary sources carefully and helps in understand few methods/technique’s for text mining.
Cite this article:
B Uday Jaswanth Redddy, CHA Raghu Rami Reddy, B Kavya. B Uday Jaswanth Redddy, CHA Raghu Rami Reddy, B Kavya. Research Journal of Engineering and Technology. 2023; 14(1):1-6. doi: 10.52711/2321-581X.2023.00001
Cite(Electronic):
B Uday Jaswanth Redddy, CHA Raghu Rami Reddy, B Kavya. B Uday Jaswanth Redddy, CHA Raghu Rami Reddy, B Kavya. Research Journal of Engineering and Technology. 2023; 14(1):1-6. doi: 10.52711/2321-581X.2023.00001 Available on: https://ijersonline.org/AbstractView.aspx?PID=2023-14-1-1
REFERENCES:
1. R. Sagayam, A survey of text mining: Retrieval, extraction and in- dexing techniques, International Journal of Computational Engineering Research, vol. 2, no. 5, 2012.
2. N. Padhy, D. Mishra, R. Panigrahi et al., “The survey of data mining applications and feature scope,” arXiv preprint arXiv:1211.5723, 2012.
3. W. Fan, L. Wallace, S. Rich, and Z. Zhang, “Tapping the power of text mining,” Communications of the ACM, vol. 49, no. 9, pp. 76–82, 2006.
4. S. M. Weiss, N. Indurkhya, T. Zhang, and F. Damerau, Text mining: predictive methods for analyzing unstructured information. Springer Science and Business Media, 2010.
5. S.-H. Liao, P.-H. Chu, and P.-Y. Hsiao, “Data mining techniques and applications–a decade review from 2000 to 2011,” Expert Systems with Applications, vol. 39, no. 12, pp. 11 303–11 311, 2012.
6. W. He, “Examining students online interaction in a live video streaming environment using data mining and text mining,” Computers in Human Behavior, vol. 29, no. 1, pp. 90–102, 2013.
7. G. King, P. Lam, and M. Roberts, “Computer-assisted keyword and document set discovery from unstructured text,” Copy at http://j. mp/1qdVqhx Download Citation BibTex Tagged XML Download Paper, vol. 456, 2014.
8. N. Zhong, Y. Li, and S.-T. Wu, “Effective pattern discovery for text mining,” IEEE transactions on knowledge and data engineering, vol. 24, no. 1, pp. 30–44, 2012.
9. A. Henriksson, H. Moen, M. Skeppstedt, V. Daudaravicˇius, and M. Duneld, “Synonym extraction and abbreviation expansion with ensembles of semantic spaces,” Journal of biomedical semantics, vol. 5, no. 1, p. 1, 2014.
10. B. Laxman and D. Sujatha, “Improved method for pattern discovery in text mining,” International Journal of Research in Engineering and Technology, vol. 2, no. 1, pp. 2321–2328, 2013.
11. C. P. Chen and C.-Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on big data,” Information Sci- ences, vol. 275, pp. 314–347, 2014.
12. R. Rajendra and V. Saransh, “A Novel Modified Apriori Approach for Web Document Clustering,” International Journal of Computer Applications, pp. 159–171, 2013.
13. K. Sumathy and M. Chidambaram, “Text mining: Concepts, applica- tions, tools and issues-an overview,” International Journal of Computer Applications, vol. 80, no. 4, 2013.
14. P. J. Joby and J. Korra, “Accessing accurate documents by min- ing auxiliary document information,” in Advances in Computing and Communication Engineering (ICACCE), 2015 Second International Conference on. IEEE, 2015, pp. 634–638.
15. Z. Wen, T. Yoshida, and X. Tang, “A study with multi-word feature with text classification,” in Proceedings of the 51st Annual Meeting of the ISSS-2007, Tokyo, Japan, vol. 51, 2007, p. 45.
16. V. Gupta and G. S. Lehal, “A survey of text mining techniques and applications,” Journal of emerging technologies in web intelligence, vol. 1, no. 1, pp. 60–76, 2009.
17. R. Agrawal and M. Batra, “A detailed study on text mining techniques,” International Journal of Soft Computing and Engineering (IJSCE) ISSN, pp. 2231–2307, 2013.
18. D. S. Dang and P. H. Ahmad, “A review of text mining techniques associated with various application areas,” International Journal of Science and Research (IJSR), vol. 4, no. 2, pp. 2461–2466, 2015.
19. R. Steinberger, “A survey of methods to ease the development of highly multilingual text mining applications,” Language Resources and Evaluation, vol. 46, no. 2, pp. 155–176, 2012.
20. A. M. Cohen and W. R. Hersh, “A survey of current work in biomedical text mining,” Briefings in bioinformatics, vol. 6, no. 1, pp. 57–71, 2005.
21. E. A. Calvillo, A. Padilla, J. Mun ̃oz, J. Ponce, and J. T. Fernan- dez, “Searching research papers using clustering and text mining,” in Electronics, Communications and Computing (CONIELECOMP), 2013 International Conference on. IEEE, 2013, pp. 78–81.
22. B. L. Narayana and S. P. Kumar, “A new clustering technique on text in sentence for text mining,” IJSEAT, vol. 3, no. 3, pp. 69–71, 2015.
23. B. A. Mukhedkar, D. Sakhare, and R. Kumar, “Pragmatic analysis based document summarization,” International Journal of Computer Science and Information Security, vol. 14, no. 4, p. 145, 2016.
24. R. Al-Hashemi, “Text summarization extraction system (tses) using extracted keywords.” Int. Arab J. e-Technol., vol. 1, no. 4, pp. 164– 168, 2010.
25. I. H. Witten, K. J. Don, M. Dewsnip, and V. Tablan, “Text mining in a digital library,” International Journal on Digital Libraries, vol. 4, no. 1, pp. 56–59, 2004.
26. S. Ayesha, T. Mustafa, A. R. Sattar, and M. I. Khan, “Data mining model for higher education system,” Europen Journal of Scientific Research, vol. 43, no. 1, pp. 24–29, 2010.
27. A. Henriksson, J. Zhao, H. Dalianis, and H. Bostro ̈m, “Ensembles of randomized trees using diverse distributed representations of clinical events,” BMC Medical Informatics and Decision Making, vol. 16, no. 2, p. 69, 2016.
28. I. Alonso and D. Contreras, “Evaluation of semantic similarity metrics applied to the automatic retrieval of medical documents: An umls approach,” Expert Systems with Applications, vol. 44, pp. 386–399, 2016.
29. C. Ding and H. Peng, “Minimum redundancy feature selection from microarray gene expression data,” Journal of bioinformatics and com- putational biology, vol. 3, no. 02, pp. 185–205, 2005.
30. Y. Zhao, “Analysing twitter data with text mining and social network analysis,” in Proceedings of the 11th Australasian Data Mining and Analytics Conference (AusDM 2013), 2013, p. 23.
31. F. Fatima, Z. W. Islam, F. Zafar, and S. Ayesha, “Impact and usage of internet in education in pakistan,” European Journal of Scientific Research, vol. 47, no. 2, pp. 256–264, 2010.
32. R. Sharda and M. Henry, “Information extraction from interviews to obtain tacit knowledge: A text mining application,” AMCIS 2009 Proceedings, p. 283, 2009.
33. H. Solanki, “Comparative study of data mining tools and analysis with unified data mining theory,” International Journal of Computer Applications, vol. 75, no. 16, 2013.
34. A. Kumaran, R. Makin, V. Pattisapu, and S. E. Sharif, “Automatic extraction of synonymy information:-extended abstract,” OTT06, vol. 1, p. 55, 2007.
35. A. Kaklauskas, M. Seniut, D. Amaratunga, I. Lill, A. Safonov, N. Vatin, J. Cerkauskas, I. Jackute, A. Kuzminske, and L. Peciure, “Text analytics for android project,” Procedia Economics and Finance, vol. 18, pp. 610–617, 2014.
36. N. Samsudin, M. Puteh, A. R. Hamdan, and M. Z. A. Nazri, “Immune based feature selection for opinion mining,” in Proceedings of the World Congress on Engineering, vol. 3, 2013, pp. 3–5.