Terrorism Detection Model using Naive Bayes Classifier

International Journal of Computer Science and Engineering
© 2020 by SSRG - IJCSE Journal
Volume 7 Issue 12
Year of Publication : 2020
Authors : Francisca Onaolapo Oladipo, Ogunsanya Funmilayo Blessing, Ezendu Ariwa

How to Cite?

Francisca Onaolapo Oladipo, Ogunsanya Funmilayo Blessing, Ezendu Ariwa, "Terrorism Detection Model using Naive Bayes Classifier," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 12, pp. 9-15, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I12P103


The advancement in microblogging has brought an increasing area of interest in sentiment analysis. Terrorist groups have been involved in using social media sites like YouTube, Facebook, and Twitter to propagate their ideology and recruitment of individuals. This work aims to propose a terrorism-related content analysis framework focusing on classifying tweets into terrorist and non-terrorist classes. Based on user-generated social media posts on Twitter, we developed a tweet classification system using supervised learning-based sentiment analysis techniques to classify the tweets as terrorist or non-terrorist. Our results indicate that an automated approach to aid analysts in detecting terrorism content on social media is a promising way forward.


Classification, Naïve bayes, classification, text Mining, terrorism.


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