In this research, we show that Online Social Networks (OSNs) can be used to study crime detection problems. Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. Thus, this happened by often use slang expressions, threats and suicide posts. For that this study use the data mining followed By Sentiment Analysis on OSNs, to help detect crime patterns by designing a filter to extract tweets , Fb statuses and group blogs taking into consideration the geographical Analysis between these tweets and the crimes in the corresponding locations. This work carries out a case study to evaluate the framework with over 300 crime –related tweets that are collected over a defined period. Sentiment analysis techniques were conducted on these tweets and statuses to analyze the crime intensity of a particular location. This work will 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 in accuracy unless if it is combined by Video-to-text processing, image-to-text processing, and data from various online sources that would also help to improve accuracy.