Analyzing News Summaries for Identification of Terrorism Incident Type

In this paper we present experiments for the detection of terrorism incident types from news summary. The news summaries from the global terrorism dataset have been analyzed using machine learning techniques. We have conducted experiments using different learning algorithms including Naive Bayes, decision tree and support vector machine. The results of the experiments show that decision tree learning algorithm can well learn incdent types from the news summary and achives high accuracy for detecting the type of incident from the news.