Text mining and machine learning for crime classification: using unstructured narrative court documents in police academic

This paper proposes a novel approach to utilizing open-source legal databases in academic education, especially in the fields of law and police investigations. Our framework provides a way to organize and analyze this data and extract reports that are associated with crime scenes, addressing the challenge of classifying unstructured legal documents by using text mining, natural language processing, and machine learning techniques. We developed a supervised machine learning model capable of accurately classifying court documents based on two classifiers: one identifies the documents containing crime scenes, and the other classifies them into five types of crimes. The experimental results were promising, as the random forest algorithm achieved an accuracy of 91.07% for the first classifier and support vector machines achieved an accuracy of 82.46% for the second classifier. What distinguishes our work is the creation of a crime dictionary that includes 70 crime tools and 151 related terms extracted from various forensic sources. It is considered relatively small, but it contributed to giving good classification results. The proposed crime dictionary can be generalized, developed, used in advanced searches, and integrated with police databases to improve crime scene analysis. Overall, the research highlights the use of court databases in police academic education and attempts to utilize them in a more effective manner.