International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 2212 Terror Attack Identifier: Classify using KNN, SVM, Random Forest algorithm and alert through messages Abhishek Barve 1 , Manali Rahate 2 , Ayesha Gaikwad 3 , Priyanka Patil 1 Assistant Professor, Vidyalankar Institute of Technology, Mumbai, India 2,3,4 Students, Vidyalankar Institute of Technology, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The system to prevent terrorist attacks that will relay emergency alerts at all phones is set to begin .This system could warn people of terrorist strikes by text messages by broadcasting it to all the people in the nearby location. With the popularity of social networks , mostly news providers used to split their news in various social networking sites and web blogs. Machine learning techniques will be used to train the data .In order to create the instances words from each short message were consider and bag-of-words approach was used to create feature vector .The data was trained using KNN(K-Nearest Neighbor), Support vector machine, Random forest machine learning techniques. Key Words: Data Mining, Twitter, news, text analysis, terrorist attack, tweets. 1. INTRODUCTION Now-a-days in India, there are many news groups who share their news headlines as short messages in micro blogging services such as Twitter. Authors of these messages write about their life, share opinions on variety of topics and discuss current issues. Because of a free format of messages and an easy accessibility of micro blogging platforms, Internet users tend to shift from traditional communication tools (such as traditional blogs or mailing lists) to micro blogging services. As more and more users post about products and services they use, or express their political and religious views, micro blogging web-sites become valuable sources of people’s opinions and sentiments. We use a dataset formed of collected messages from Twitter. Twitter contains a very large number of very short messages created by the users of this micro blogging platform. The contents of the messages vary from personal thoughts to public statements. The short messages will be classified by the system into a group: war- terrorist-crime. 1.1 Objectives To develop a system that will extract the live tweets from twitter site, will classify those tweets and display the news under its section that will help news seeker to keep track of news. For development of the proper system a perfect classifier has to be selected that can be done by comparing different classifier result on tweets provided. 1.2 Scope Scope of this dissertation is to develop a system that will collect short messages from twitter social networking site. The collected twitter messages are used to train by using SVM, Random Forest and KNN data mining techniques and a classifier is built that will classify the messages (e.g. war- terrorist).The performance of each classification techniques is calculated that will be the effectiveness of the system. Thus precision and recall values are calculated to measure the performance of each classifier system. F_β was calculated to obtain a single value measurement. The results generated from all 3 classifiers is compared in order to find the classifier that provides high performance for most groups will be consider as the best classifier for classifying the messages extracted from twitter, so that users or analyst in specific field able to know about the news 1.3 Proposed system We are using K-Nearest Neighbour data mining method for classifying twitter message into new group. This Chapter deals with the study which involves detail knowledge of twitter, Web Mining, data gathering techniques for tweets extraction, feature selection technique and detail of classification algorithms used for extraction. 2. Implementation This shows how the system is implemented. For this first module extract the tweets from the trusted news channel that is the input for the system. The output module gives the result in the form of tweets classified in news group: war- terrorist-crime, economy business, health, sports development-government, politics, accident, entertainment, disaster-climate, education, society and international. For the classification KNN, SVM and Random Forest are used, twits are classified and analysis in done on the result drawn from all three algorithms is shown in order to find the best classifier for the twit’s classification. Data Gathering The classification will be applied into the short messages- news of Twitter micro blog. Thus, twitter short messages are needed to be collected. Twitter API provides the ability of retrieving such short messages for a given user in XML file format. Each XML file could carry out 200 short messages at once.