Correlating Crime and Social Media: Using Semantic Sentiment Analysis

Abstract

Crimes occur all over the world and with regularly changing criminal strategies, law enforcement agencies need to manage them adequately and productively. If these agencies have prior data on the crime or an early indication of the eventual felonious activity, it would encourage them to have some strategic preferences so that they can deploy their restricted and elite assets at the spot of a suspected crime or even better explore it to the point of anticipation. So, integration of social media content can act as a catalyst in bridging the gap between these challenges as we are aware of the fact that almost all our population uses social media and their life, thoughts, and, mindset are available digitally through their social media profiles. In this paper, an attempt has been made to predict crime pattern using geo-tagged tweets from five regions of India. We hypothesized that publicly available data from Twitter may include features that can portray a correlation between Tweets and the Crime pattern using Data Mining. We have further applied Semantic Sentiment Analysis using Bi-directional Long Short memory (BiLSTM) and feed forward neural network to the tweets to determine the crime intensity across a region. The performance of our prosed approach is 84.74 for each class of sentiment. The results showed a correlation between crime pattern predicted from Tweets and actual crime incidents reported.