Leveraging Machine Learning Methods for Crime Analysis in Textual Data

Abstract

The proposed research paper explores the application of machine learning techniques in crime analysis problem, specifically focusing on the classification of crime-related textual data. Through a comparative analysis of various machine learning models, including traditional approaches and deep learning architectures, the study evaluates their effectiveness in accurately detecting and categorizing crime-related text data. The performance of the models is assessed using rigorous evaluation metrics, such as the area under the receiver operating characteristic curve (AUC-ROC), to provide insights into their discriminative power and reliability. The findings reveal that machine learning frameworks, particularly the deep learning model, consistently outperform conventional machine learning approaches, highlighting the potential of advanced neural network architectures in crime analysis tasks. The implications of these findings for law enforcement agencies and researchers are discussed, emphasizing the importance of leveraging advanced machine learning techniques to enhance crime prevention and intervention efforts. Furthermore, avenues for future research are identified, including the integration of multiple data sources and the exploration of interpretability and explainability of machine learning models in crime analysis problem. Overall, this research contributes to advancing the field of crime analysis problem and underscores the importance of leveraging innovative computational approaches to address complex societal challenges.