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Support Vector Machine and Naïve Bayes comparison of Sentiments on Terrorism

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Muhammad Umer Haroon

Muhammad Umer Haroon. Support Vector Machine and Naïve Bayes comparison of Sentiments on Terrorism. International Journal of Computer Applications 179(17):15-17, February 2018. BibTeX

	author = {Muhammad Umer Haroon},
	title = {Support Vector Machine and Naïve Bayes comparison of Sentiments on Terrorism},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2018},
	volume = {179},
	number = {17},
	month = {Feb},
	year = {2018},
	issn = {0975-8887},
	pages = {15-17},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2018916022},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Text Analysis has become a major area of research. In order to be aware of what people think and how they feel after terrorism attacks, there needs to be some mechanism. We aim to propose a solution in this regard to learn about people's sentiments in detail on terrorism incidents in Pakistan using text analysis. In this research support vector machines and naïve Bayes algorithms are compared in finding out the sentiments from data set of opinions express on terrorism activities in Pakistan.


  1. H. e. a. Woo, "Public trauma after the Sewol Ferry disaster: The role of social media in understanding the public mood.," International journal of environmental research and public health, vol. 12, no. 9, pp. 10974-10983, 2015
  2. P. P. Pak, "Twitter as a corpus for sentiment analysis and opinion mining.," vol. 10, 2010
  3. L. a. F. E. A. a. S. S. D. a. Y. S. a. L. L. T. ,. D. J. ,. N. A. ,. L. Kavanaugh, "Social media use by government: From the routine to the critical," Government Information Quarterly, vol. 29, pp. 480-491, 2012
  4. N. ,. S. Memon, "Analyzing news summaries for identification of terrorism incident type," Educational Research International (erint.), vol. 3, no. 4, pp. 81-88, 2014
  5. ,. S. L. Z. Haarmann, "Applied Text Mining for Military Intelligence Necessities," in Proceedings of the 6th Future Security Conference, Berlin, 2011.
  6. W. E. a. C. J. M. ,. E. L. ,. B. K. ,. K. M. ,. D. a. T. L. ,. D. J. M. ,. F. J. A. ,. R. A. Schlenger, "Psychological reactions to terrorist attacks: findings from the National Study of Americans' Reactions to September 11," American Medical Association, vol. 288, no. 5, pp. 581-588, 2002
  7. ,. A. M. a. P. H. ,. I. M. Neviarouskaya, "Intelligent interface for textual attitude analysis," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 3, p. 48, 2014.
  8. worst-terrorist-attacks-pakistans-military-forces," yumtoyikes, [Online]. Available: [Accessed 10 2 2016].


Sentiments, Text analysis, terrorism incidents.