A decision support system: Automated crime report analysis and classification for e-government
This paper investigates how text analysis and classification techniques can be used to enhance e-government, typically law enforcement agencies' efficiency and effectiveness by analyzing text reports automatically and provide timely supporting information to decision makers. With an increasing number of anonymous crime reports being filed and digitized, it is generally difficult for crime analysts to process and analyze crime reports efficiently. Complicating the problem is that the information has not been filtered or guided in a detective-led interview resulting in much irrelevant information. We are developing a decision support system (DSS), combining natural language processing (NLP) techniques, similarity measures, and machine learning, i.e., a Nave Bayes' classifier, to support crime analysis and classify which crime reports discuss the same and different crime. We report on an algorithm essential to the DSS and its evaluations. Two studies with small and big datasets were conducted to compare the system with a human expert's performance. The first study includes 10 sets of crime reports discussing 2 to 5 crimes. The highest algorithm accuracy was found by using binary logistic regression (89%) while Naive Bayes' classifier was only slightly lower (87%). The expert achieved still better performance (96%) when given sufficient time. The second study includes two datasets with 40 and 60 crime reports discussing 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.
classification e-government natural language processing similarity measures measurement algorithms