Crime index based on text mining on social media using multi classifier neural-net algorithm

Abstract

Everyday criminal issues appear on social media, even some crime news is often disturbing to the public but it gives a warning to the public to remain careful and alert to the surrounding environment. However, following large amounts of crime information on social media is not effective, especially for busy people. Therefore, there is a need to efficiently and effectively summarize information in a way that is meaningful and easy to see, attracts people’s attention, and can be used by law enforcement officials. The purpose of this study is to present the index crime based on social media by looking for patterns of crime. This study proposes the projected index crime based on crime trends by using text mining to classify tweet texts and post contents into 10 crime classes. The classification method uses the neural-net multi classifier algorithm which has several classifiers namely logistic regression, naïve bayes, support vector machine (SVM), and decision tree in parallel. In this approach, the classifier that provides the best accuracy will be the winning classifier and will be used in the next learning process. In this experiment, in using the multi classifier neural-net, the logistics regression classifier often provides the best accuracy.