A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

Abstract

The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings.