Automated crime report analysis and classification for e-government and decision support

Abstract

With an increasing number of anonymous crime tips and reports being filed and digitized, it is generally difficult for crime analysts to process and analyze crime reports efficiently. We are developing a decision support system (DSS), combining Natural Language Processing (NLP) techniques, a document similarity measure, and machine learning, i.e., a Naive Bayes' classifier, to support crime analysis and classify which crime reports discuss the same and different crime. The DSS is developed with text mining techniques and evaluated with an active crime analyst. We report here on an experiment that includes two datasets with 40 and 60 crime reports and 16 different types of crimes for each dataset. The results show that our system achieved the highest classification accuracy (94.82%), while the crime analyst's classification accuracy (93.74%) is slightly lower.