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Reseach Article

Reviewing the Pathway of Text-Mining Approaches to Gauge the Applicability in Data Analysis

by Jalaja G., Sajitha N., K.R.Udaya Kumar Reddy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 3
Year of Publication: 2015
Authors: Jalaja G., Sajitha N., K.R.Udaya Kumar Reddy
10.5120/ijca2015905854

Jalaja G., Sajitha N., K.R.Udaya Kumar Reddy . Reviewing the Pathway of Text-Mining Approaches to Gauge the Applicability in Data Analysis. International Journal of Computer Applications. 125, 3 ( September 2015), 10-15. DOI=10.5120/ijca2015905854

@article{ 10.5120/ijca2015905854,
author = { Jalaja G., Sajitha N., K.R.Udaya Kumar Reddy },
title = { Reviewing the Pathway of Text-Mining Approaches to Gauge the Applicability in Data Analysis },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 3 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number3/22411-2015905854/ },
doi = { 10.5120/ijca2015905854 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:15:33.768155+05:30
%A Jalaja G.
%A Sajitha N.
%A K.R.Udaya Kumar Reddy
%T Reviewing the Pathway of Text-Mining Approaches to Gauge the Applicability in Data Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 3
%P 10-15
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the increasing usage of mobile network and advancement in telecommunication standards, there is a massive growing of social networks those shares and exchanges giant forms of data. Although such forms of information are in various forms e.g. audio, video, text, image, or some specific file formats, but majorities of the transactional data are still in the form of text. Although there is various researches works being carried out in the area of datamining as well as text mining for more than a decade, the commercial usage of such tools is still not practiced owing to various challenges that are unsolved till date. Hence, the prime motive of this paper is to discuss about the fundamentals of text-mining and various significant issues associated with it. It also discusses about some of the review studies being discussed till date on same topic and updates the existing system by presenting more recent information of studies carried out towards text-mining most recently. Finally, the paper discusses exclusively the limitation explored in the existing system and then discusses about the research gap.

References
  1. Miner, G.2012. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Books, Academic Press, Mathematics, pp.1053
  2. Weiss,S.M., Indurkhya, N., Zhang, T., Damerau, F.2010.Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer Science & Business Media, Computers, pp.237
  3. Han, J., Kamber, M., Pei, J.2011.Data Mining: Concepts and Techniques: Concepts and Techniques. Elsevier, Computers, pp.744
  4. Mehler, A., Kühnberger,K-Uwe., Lobin, H., Lüngen,H., Storrer,A., Witt,A.2011.Modeling, Learning, and Processing of Text-Technological Data Structures. Springer, Mathematics, pp.400
  5. Fiori, A.2014.Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding: Revolutionizing Knowledge Understanding.IGI Global, Computers, pp.363
  6. Moreno.J-M. T.2014.Automatic Text Summarization. John Wiley & Sons, Computers, pp.320, 2014
  7. Liu, B.2012.Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, Language Arts & Disciplines, pp.167
  8. Ahmad, K.2011.Affective Computing and Sentiment Analysis: Emotion, Metaphor and Terminology. Springer Science & Business Media
  9. NissanE.2012.Computer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation. Springer Science & Business Media, Social Science, pp.1340
  10. Karthikeyan,M.,Vyas,R.2014.PracticalChemoinformatics.Springer, 20Cheminformatics, pp. 33
  11. Berry,M.W., Kogan, J.2010.Text Mining: Applications and Theory.John Wiley & Sons, Mathematics, pp.222
  12. Ray, A. Kumar, and Kushwaha, A. (Retrieved, 2015). Quality based Web information extraction approach using NLP and Text Mining.
  13. Gupta, V., and Lehal, G.S.2009. A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, Vol. 1, No. 1, pp. 60-76
  14. Clifton, P., Lee, V., Smith, K., and Gayler, R.2010. A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv: 1009.6119
  15. Charu, A., and Zhai, C.X.2012. A survey of text clustering algorithms. In Mining Text Data, pp. 77-128
  16. Sagayam, R., Srinivasan, S., and Roshni, S.2012. A Survey of Text Mining: Retrieval, Extraction and Indexing Techniques. International Journal of Computational Engineering Research, Vol. 2, No. 5
  17. Korde, V., and Mahender, N.C.2012. Text classification and classifiers: A survey. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 3, No. 2, pp. 85-99.
  18. Jensi, R., and Jiji, W.G.2014. A Survey on optimization approaches to text document clustering. arXiv preprint arXiv: 1401.2229
  19. Agrawal, R., and Batra, M.2013. A detailed study on text mining techniques. International Journal of Soft Computing and Engineering (IJSCE) ISSN 2231-2307.
  20. Rizwanairfan, C., King, G., Nielgragesi, D A., W en, D.,. Khan, S., Sajjdada.2015. A Survey on Text Mining in Social Networks. The Knowledge Engineering Review, Vol. 00:0, pp.1–24
  21. Saranya, S., and Munieswari, R. (Retrieved, 2015). A Survey on Improving the Clustering Performance in Text Mining for Efficient Information Retrieval. International Journal of Engineering Trends and Technology (IJETT)–Vol, 8.
  22. Ning, Z., Li, Y., and Wu, S.2012. Effective pattern discovery for text mining. Knowledge and Data Engineering, IEEE Transactions, Vol. 24, No. 1, pp. 30-44.
  23. Murali, K.S., and Bhavani, S.D.2010. An efficient approach for text clustering based on frequent itemsets. European Journal of Scientific Research, Vol.42, No. 3, pp.399-410.
  24. Mehta, R., Sankarasubramaniam, B., and Rajalakshmi, S.2012. An algorithm for fuzzy-based sentence-level document clustering for micro-level contradiction analysis. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp. 102-105
  25. Ampofo, L., Collister, S., B.O’Loughlin, and Chadwick, A. (Retrieved, 20th July, 2015). Text Mining and Social Media: When Quantitative Meets Qualitative, and Software Meets Humans. Text Mining and Social Media: When Quantitative Meets Qualitative, and Software Meets Humans
  26. Roberts, M. E., Brandon M. S., Dustin T., Christopher L., Jetson, L.L, Gadarian, S.K., Albertson, B., and David G. Rand.2014. Structural Topic Models for Open‐Ended Survey Responses. American Journal of Political Science
  27. Yuejin, X., and Reynolds, N.2011. Using text mining techniques to analyze students’ written responses to a teacher leadership dilemma. In Proceedings of the 4th IEEE International Conference on Computer Science and Information Technology, China, Vol. 4, pp. 93-97
  28. Feinerer, I.2014. Introduction to the tm Package Text Mining in R. nd): n. pag. Web
  29. Y-H. Tseng. Lin, C-J., and Lin, Y-I.2007. Text mining techniques for patent analysis. Information Processing & Management, Vol. 43, No. 5, pp. 1216-1247.
  30. Elad, S., and Miesch, R.2011. A systematic procedure for detecting news biases: The case of Israel in European news sites. International Journal of Communication, Vol. 5.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Document Clustering Text-Mining Machine Learning Pattern Recognition