Detecting Crimes Related Twitter Posts using SVM based Two Stages Filtering
S.P.C.W Sandagiri, Banage T. G. S. Kumara, Banujan Kuhaneswaran · 2020
Crime is a major problem faced today by society. Crimes have affected the quality of life and economic growth badly. We can identify the crime patterns and predict the crimes by detecting and analyzing the historical data. However, some crimes are unregistered and unsolved due to a lack of evidence. Thus, detecting crimes is a still challenging task. We can use social media like Twitter to detect crimes related activities. Because Twitter users sometimes convey messages related to his or her surrounding environment via Twitter. In this paper, we proposed a machine learning approach to detect the crimes and the location of the crimes. As the first step, we fetch the Twitter posts using predefined keywords relating to the crimes. Then, after the preprocessing, we applied a support vector machine-based filtering approach to eliminate the noise. Then in the final stage, we get the geolocation and crime type. The empirical study of our prototyping system has proved the effectiveness of our proposed crime detection approach.