, Prof. S.R Asati, Kajal Diwe, Rajshri Yadav, Ravi Shambharkar

English

International Journal on Future Revolution in Computer Science & Communication Engineering

International Journal on Future Revolution in Computer Science & Communication Engineering                                       ISSN: 2454-4248 Volume: 4 Issue: 3                                                                                                                                                                           354 – 356 _______________________________________________________________________________________________ 354 IJFRCSCE | March 2018, Available @ http://www.ijfrcsce.org                                                                 _______________________________________________________________________________________ Cyber-Bullying Text Detection and Removal    Prof. S.R Asati   (Guide), Kajal Diwe Computer science & Engineering,Dept. of Computer science and engineering S.S.P.A.C.E, Wardha (MS)  S.S.P.A.C.E, Wardha (MS) Kajaldiwe46@gmail.com  Rajshri Yadav,   Ravi Shambharkar Dept. of Computer science and engineering, Dept. of Computer science and engineering, S.S.P.A.C.E, Wardha (MS)S.S.P.A.C.E, Wardha (MS) rajshriyadav@gmail.com  I. Introduction The Currently, social network systems are online platforms that people use to build social networks or social relations with other people who share similar personal or career interests, activities, backgrounds or real-life connections. Used for real time files sharing, info. Sharing, connecting within the communities.  Since their introduction, social network sites (SNSs) such as Myspace, Facebook, Cyworld, and Bebo have attracted millions of users, many of whom have integrated these sites into their daily practices. As of this writing, there are hundreds of SNSs, with various technological affordances, supporting a wide range of interests and practices. While their key technological features are fairly consistent, the cultures that emerge around SNSs are varied. Most sites support the maintenance of pre-existing social networks (Danah M. Boyd and Nicole B. Ellison). There are over 100 million worldwide views per day on YouTube .There are more Facebook users (350 million) than there are U.S. residents (309 million). Twitter will process about 10 billion relentless.  Not only does it go on after school, college or work has finished, but it then carries through into the next day and the cycle continues.  It has been well documented that cyber bullying has resulted in tragic events including suicide, and self-harm and clearly, more needs to be done in order to protect vulnerable children and adults from online bullying. Hence, recognizing the cyberbullying event itself is not efficient in combating cyberbullying per se, as we need to identify the real user of the cyberbully in order to arrest them for justice, and to prevent further similar cases to happen.  Thus, it gave us an idea to create a web-based application; which is cyberbullying detection and removal system. With the primary function of detecting and removal of cyberbullying related tweets and provide rigor and satisfying solution after detecting cyberbullying tweets or comments, With our system user can detect cyberbullying tweets or comments and report 0it to admin who will populate news feed with that tweet and add specific cyberbullying keywords to the application’s database. Admin will also take some responsible actions against the accounts which are spreading cyberbullying tweets and comments.  In conclusion, our system will give rights and power to monitor, regulate and take action against the cyberbullies. Which will definitely result in decrement of harassing anyone over the internet.   II. Literature survey Sr.  No. Title  Author  Year Summary 1 Tweet Segmentation and Its Application to Named Entity Recognition Chenliang Li, Aixin Sun FEBRUARY 2015 In this paper  tweet segmentation is defined where it explain how to recognize tweets.  International Journal on Future Revolution in Computer Science & Communication Engineering                                       ISSN: 2454-4248 Volume: 4 Issue: 3                                                                                                                                                                           354 – 356 _______________________________________________________________________________________________ 355 IJFRCSCE | March 2018, Available @ http://www.ijfrcsce.org                                                                 _______________________________________________________________________________________ 2 Tweet Segmentation and Named Entity Recognition ChetanChavan, Prof. RanjeetsinghSuryawanshi 1 –JANUARY 2016 It defined how to extract information from huge quantity of tweets on the basis of NER. 3 A Review Paper on Tweet segmentation and its Application to Named EntityRecognition Anuja A. Thete, Prof.J.S. Karnewar January 2016 In this paper, they propose a novel framework for tweet segmentation in a batch mode, called HybridSeg by splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. 4 International Journal of Innovative Research in Computer and Communication Engineering AkshayShinde, SachinYelmar 4, April 2016 It proposed a system that achieves high accuracy of segmentation based on named entity recognition.  Sr. No. Title  Author  Year Summary 9 Enhancing Named Entity Recognition in Twitter Messages Using Entity Linking. Ikuya Yamada, Hideaki Takeda July 31, 2015 The author propose a novel method to enhance the performance of the Twitter NER task by using Entity Linking which is a method for detecting entity mentions in text and resolving them to corresponding entries in knowledge base 10 Survey on Named Entity Recognition System over Twitter Data. MinalS.Sonmal,Prof. RajaramH.Ambole December 2015 In this the Real time applications is proposed depending on name entity recognition. 11 NEED4Tweet: A Twitterbot for Tweets Named Entity Extraction and Disambiguation. Mena B. Habib, Maurice van Keulen May 2013 In this paper, author introduce a novel framework for extracting syntactic features is used to analyze the text. International Journal on Future Revolution in Computer Science & Communication Engineering                                       ISSN: 2454-4248 Volume: 4 Issue: 3                                                                                                                                                                           354 – 356 _______________________________________________________________________________________________ 356 IJFRCSCE | March 2018, Available @ http://www.ijfrcsce.org                                                                 _______________________________________________________________________________________ 12 TwiNER: Named Entity Recognition in Targeted Twitter Stream. Chenliang Li, JianshuWeng August 2012 In this paper, we present a novel 2-step unsupervised NER system for targeted Twitter stream, called TwiNER. And it is used to partition tweets into valid segments (phrases)  Proposed Architecture  III. Conclusion According to our research, our system will help many organizations and individual entities to detect bullying words which will make any social network site a decent place. The application will capture specific bullying tweets and comments. The system will fetch the matched tweets with the cyberbullying-related words, and the basic profile information of cyberbullying users (cyber bully and/or victims) from the database. Location of the computer of cyber bully will be sent out to the admin. And then legal action against cyber bully will be taken  

Cyber-Bullying Text Detection and Removal

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Cyber-Bullying Text Detection and Removal

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International Journal on Future Revolution in Computer Science & Communication Engineering