Journal of Engineering and Applied Sciences

Year: 2017
Volume: 12
Issue: 3
Page No. 691 - 698

Terrorism Detection Based on Sentiment Analysis Using Machine Learning

Authors : Sofea Azrina Azizan and Izzatdin Abdul Aziz

References

Berger, J.M. and J. Morgan, 2015. The ISIS Twitter census defining and describing the population. Brookings Project U.S. Relat. Islamic World, 3: 1-65.

Blinov, P., M. Klekovkina, E. Kotelnikov and O. Pestov, 2013. Research of lexical approach and machine learning methods for sentiment analysis. Comput. Linguistics Intellectual Technol., 2: 48-58.

Concordiam, P., 2014. Extremism hits home stopping the spread of terrorism. J. Eur. Secur. Defense Issues, 5: 1-67.

Feldman, R., 2013. Techniques and applications for sentiment analysis. Commun. ACM., 56: 82-89.
CrossRef  |  Direct Link  |  

Goncalves, P., M. Araujo, F. Benevenuto and M. Cha, 2013. Comparing and combining sentiment analysis methods. Proceedings of the 1st ACM Conference on Online Social Networks, October 07-08, 2013, ACM, Boston, Massachusetts, ISBN:978-1-4503-2084-9, pp: 27-38.

Hailong, Z., G. Wenyan and J. Bo, 2014. Machine learning and lexicon based methods for sentiment classification: A survey. Proceedings of the 11th Conference on Web Information System and Application Conference (WISA), September 12-14, 2014, IEEE, Nanjing, China, ISBN:978-1-4799-5726-2, pp: 262-265.

Hassan, S., H. Yulan and A. Harith, 2012. Alleviating data sparsity for Twitter sentiment analysis. Proceedings of the 2nd Workshop on Making Sense of Microposts: Big Things Come in Small Packages and 21st International Conference on World Wide Web, April 16, 2012, CEUR, Lyon, France, pp: 2-9.

Khan, A.Z., M. Atique and V.M. Thakare, 2015. Combining lexicon-based and learning-based methods for Twitter sentiment analysis. Int. J. Electron. Commun. Soft Comput. Sci. Eng., 2015: 89-91.
Direct Link  |  

Liu, B. and L. Zhang, 2012. A Survey of Opinion Mining and Sentiment Analysis. In: Mining Text Data, Aggarwal, C.C. and C.X. Zhai (Eds.). Springer, New York, USA., ISBN:978-1-4614-3222-7, pp: 415-463.

Liu, B., 2010. Sentiment Analysis and Subjectivity. In: To Appear in Handbook of Natural Language Processing, Indurkhya, N. and F.J. Damerau (Eds.). 2nd Edn., University of Illinois, Chicago, USA., pp: 1-38.

Medhat, W., A. Hassan and H. Korashy, 2014. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J., 5: 1093-1113.
CrossRef  |  Direct Link  |  

Mergel, I., 2013. Social media adoption and resulting tactics in the US federal government. Government Inf. Q., 30: 123-130.
Direct Link  |  

Narayanan, V., I. Arora and A. Bhatia, 2013. Fast and accurate sentiment classification using an enhanced Naive Bayes model. Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, October 20-23, 2013, Springer, Berlin, Germany, pp: 194-201.

RFIC., 2016. Explosive growth in ISIS tweets: Arabic overtakes English. Recorded Future Internet Company, Somerville, Massachusetts, USA. https://www.recordedfuture.com/isis-twitter-growth/

Ramendran, C., 2016. Cop among 15 detained for IS militancy. The Sun Daily, Malaysia. http://www.thesundaily.my/news/1739087

Ray, S., 2015. 6 East steps to learn naive bayes algorithm. Analytics Vidhya, USA. https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/

Santos, C.N.D. and M. Gatti, 2014. Deep convolutional neural networks for sentiment analysis of short texts. Proceedings of the COLING 25th International Conference on Computational Linguistics, August 23-29, 2014, Dublin City University, Dublin, Ireland, pp: 69-78.

Thakkar, H. and D. Patel, 2015. Approaches for sentiment analysis on Twitter: A state-of-art study. Department of Computer Engineering, Montreal, Quebec, Canada.

Toit, J.D., 2015. The Bayes classifier: Building a tweet sentiment analysis tool. Cloud Academy Blog, USA. http://cloudacademy.com/blog/naive-bayes-classifier/

USDHS., 2011. Analyst's desktop binder. Department of Homeland Security National Operations Center, Media Monitoring Capability, Desktop Reference Binder, USA.

Wahari, H., 2016. Malaysia arrests 15 alleged IS supporters. Benar News, Kuala Lumpur, Malaysia. http://www.benarnews.org/english/news/malaysian/suspects-arrest-03242016144757.html

Xhemali, D., C.J. Hinde and R.G. Stone, 2009. Naive Bayes vs. Decision trees vs. Neural networks in the classification of training web pages. Intl. J. Comput. Sci. Issues, 4: 16-23.
Direct Link  |  

Yadron, D., 2016. Twitter deletes 125,000 Isis accounts and expands anti-terror teams. The Guardian, London, England, UK.

Younas, M.A., 2014. Digital jihad and its significance to counterterrorism. Counter Terrorist Trends Anal., 6: 10-17.

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