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Improving Accuracy of Text Classification for SMS Data

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Hiral D. Padhiyar, Dilipsinh N. Padhiar
10.5120/ijca2017914429

Hiral D Padhiyar and Dilipsinh N Padhiar. Improving Accuracy of Text Classification for SMS Data. International Journal of Computer Applications 169(1):19-21, July 2017. BibTeX

@article{10.5120/ijca2017914429,
	author = {Hiral D. Padhiyar and Dilipsinh N. Padhiar},
	title = {Improving Accuracy of Text Classification for SMS Data},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {1},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {19-21},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume169/number1/27949-2017914429},
	doi = {10.5120/ijca2017914429},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Text classification has become one of the major techniques for organizing and managing online information; similarly SMS classification is also an important task now a day. In this paper, we have focused on the issue of short words used in SMS (hpy for happy, bday for birthday) which reduces classification accuracy, so after removing such words with original words, we got better accuracy. We used Decision tree Algorithm for classification of SMS data as it is giving better accuracy then other classifiers. But still replacing all possible short words for the given word dynamically by the original word is an issue.

References

  1. Kamber, J. H. Data Mining: Concept and Techniques.
  2. M.Sukanya, S. (2012). Techniques on Text Mining. ICACCCT.
  3. Mita K. Dalal, M. A. (2011). Automatic Text Classification: A Technical Review. International Journal of Computer Applications .
  4. QASEM A. AL-RADAIDEH, E. M.-S. (2011). An Approach for Arabic Text Categorization Using Association Rule Mining. International Journal of Computer Processing Of Languages.
  5. Yun Yang, Y. W. (2010). The Improved Features Selection for Text Classification. 2nd International Conference on Computer Engineering and Technology. IEEE.

Keywords

Decision Tree, Text Classification