A Sentiment Analysis Model for Terrorist Attacks Reviews on Twitter

Twitter is considered as one of the famous microblogs that attract politicians and individuals to express their views on political, economic and social issues. The phenomenon of terrorist operations is one of the largest security and economic problem facing the world in recent years. Twitter users' comments on terrorism issues are important to understand users' sentiment about terrorist events. Sentiment analysis is a field of research for understanding and extracting users’ views. In this paper, we propose a model for automatically classifying users’ reviews on Twitter after occurrence of a terrorist attack, the model is built using lexicon and machine learning approaches. Lexicon approach is used to create labelled training dataset while machine learning approach was used to build the model. Scores of some domain related words were neutralized to avoid their negative effect. Features were selected based on PoS. Majority voting between NB, SVM and LR machine learning classification algorithms was applied. The performance of classification algorithms was measured using accuracy and F1 scores. The results obtained are compared to identify the best classification algorithm for features selection. Result show that our model achieved 94.8% accuracy with 95.9% F1 score.Keywords: Sentiment Analysis, Machine Learning, lexicon-based approach, Terrorist mining.

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[1] J. Klausen, “Tweeting the Jihad: Social media networks of Western foreign fighters in Syria and Iraq,” Stud. Confl. Terror., vol. 38, no. 1, pp. 1–22, 2015.
[2] Council of Europe, “Cyberterrorism: The Use of the Internet for Terrorist Purposes,” United Nations Off. Drugs Crime, vol. 12, no. 1, p. 497, 2007.
[3] P. Choudhary and U. Singh, “A Survey on Social Network Analysis for CounterTerrorism,” Int. J. Comput. Appl., vol. 112, no. 9, pp. 24-29, 2015.
[4] P. Burnap et al., “Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack,” Soc. Netw. Anal. Min., vol. 4, no. 1, pp. 1–14, 2014.
[5] T. Chalothorn and J. Ellman, “Using SentiWordNet and Sentiment Analysis for Detecting Radical Content on Web Forums,” Nrl.Northumbria. Ac. Uk, no. 1.
[6] A. Azizan, Sofea, Aziz, “Terrorism Detection Based on Sentiment Analysis Using Machine Learning.pdf,” J. Eng. Appl. Sci., vol. 12, no. 3, pp. 691–698, 2017.
[7] B. Liu, “Sentiment Analysis and Opinion Mining,” Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.
[8] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP), 2002, vol. 10, pp. 79–86.
[9] N. A. S. Abdullah, N. I. Shaari, and A. R. A. Rahman, “Review on sentiment analysis approaches for social media data,” Journal of Engineering and Applied Sciences, vol. 12, no. 3. pp. 462–467, 2017.
[10] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014.
[11] M. Ashcroft, A. Fisher, L. Kaati, E. Omer, and N. Prucha, “Detecting Jihadist Messages on Twitter,” Eur. Intell. Secur. Informatics Conf. EISIC 2015, pp. 161– 164, 2016.
[12] W. Magdy, K. Darwish, and I. Weber, “#FailedRevolutions: Using Twitter to study the antecedents of ISIS support,” First Monday, vol. 21, no. 2, 2016.
[13] A. J. Park, B. Beck, D. Fletche, P. Lam, and H. H. Tsang, “Temporal analysis of radical dark web forum users,” Proc. 2016 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2016, pp. 880– 883, 2016.
[14] A. H. Johnston and G. M. Weiss, “Identifying sunni extremist propaganda with deep learning,” 2017 IEEE Symp. Ser. Comput. Intell., no. December 2015, pp. 1– 6, 2017.
[15] T. B. Mirani and S. Sasi, “Sentiment analysis of ISIS related Tweets using Absolute location,” in International Conference on Computational Science and Computational Intelligence Sentiment, pp. 1140–1145, 2016.
[16] G. A. Ali, “Identifying Terrorist Affiliations through Social Network Analysis Using Data Mining Techniques,” Valparaiso University, 2016.
[17] L. Gatti, M. Guerini, and M. Turchi, “SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis,” IEEE Trans. Affect. Comput., vol. 7, no. 4, pp. 409–421, 2016.
[18] Natural Language Processing, “SentiWords | HLT-NLP.” [Online]. Available: https://hltnlp.fbk.eu/technologies/sentiwords. [Accessed: 19-Mar-2018].
[19] A. Hogenboom, P. Van Iterson, B. Heerschop, F. Frasincar, and U. Kaymak, “Determining negation scope and strength in sentiment analysis,” in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 2589–2594, 2011.
[20] C. Bhadane, H. Dalal, and H. Doshi, “Sentiment analysis: Measuring opinions,” Procedia Comput. Sci., vol. 45, no. C, pp. 808–814, 2015.
[21] J. M. Moguerza and A. Muñoz, “Support Vector Machines with Applications,” Stat. Sci., vol. 21, no. 3, pp. 322–336, 2006.
[22] J. Feng, H. Xu, S. Mannor, and S. Yan, “Robust Logistic Regression and Classification,” Neural Inf. Process. Syst., pp. 1–9, 2014.