TITLE:
Improvised Technique for Analyzing Data and Detecting Terrorist Attack Using Machine Learning Approach Based on Twitter Data
AUTHORS:
Aditi Sarker, Partha Chakraborty, S. M. Shaheen Sha, Mahmuda Khatun, Md. Rakib Hasan, Kawshik Banerjee
KEYWORDS:
Terrorist, Ternary Search, Aho-Corasick Automata, Confusion Matrix
JOURNAL NAME:
Journal of Computer and Communications,
Vol.8 No.7,
July
30,
2020
ABSTRACT: Social media are interactive computer mediated technology that facilitates the sharing of information via virtual communities and networks. And Twitter is one of the most popular social media for social interaction and microblogging. This paper introduces an improved system model to analyze twitter data and detect terrorist attack event. In this model, a ternary search is used to find the weights of predefined keywords and the Aho-Corasick algorithm is applied to perform pattern matching and assign the weight which is the main contribution of this paper. Weights are categorized into three categories: Terror attack, Severe Terror Attack and Normal Data and the weights are used as attributes for classification. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are two machine learning algorithms used to predict whether a terror attack happened or not. We compare the accuracy with our actual data by using confusion matrix and measure whether our result is right or wrong and the achieved result shows that the proposed model performs better.