Published Paper Details:

Social media is one of the greatest source, which creates high impact on grouping the public easily. So these free sources has the possibilities of making roomers and fake news to spoil an individual and leads the crime in a society. Crime is defined as an act harmful not only to the individual involved, but also to the community as a whole. Crimes are social nuisances that place heavy financial burdens on society. Here we look at use of data mining followed by sentiment analysis on online social networks, to help detect the crime patterns. Twitter is an online social networking and micro blogging service that enables users to post brief text updates, also referred to as "tweets". These updates can convey important information about the author. A filter was designed to extract tweets from cities deemed to be either the most dangerous or the safest in the United States (US). A geographic analysis revealed a correlation between these tweets and the crimes that occurred in the corresponding cities. Over 100,000 crime-related tweets were collected over a period of 20 days. Sentiment analysis techniques were conducted on these tweets to analyze the crime intensity of a particular location. This type of study will help reveal the crime rate of a location in real-time. Although the results of this test helped in detecting crime patterns, the sentiment analysis techniques did not always guarantee the proper results. We can conclude with applications of this type of study and how it can be improved by applying media to text processing techniques and also added to the system that are detect the current location of user.