Now a day, most of people are using twitter, face book and micro blogging sites. They share their opinion, feeling for particular topic through comment, review. The volume of data generated daily is very large. So, it is important to analyse the data for gaining information from that. Sentimental analysis is used for mining various types of data for opinion through text analytics. It can be negative, positive or impartial. Twitter became one of the largest platform for people to show their opinion, share their thoughts and consistently updated about any organization, events etc. So, data collected is huge somewhat called big data. To process such a big data we need framework that manages this entire thing. In this paper, we attempt to perform cyber bullying detection in a supervised way by proposing a learning framework. More specifically, we first investigate whether sentiment information is correlated with cyber bullying behavior.

Cyber bullying, social media, attacks, security, cyber crime

[1] Ditch the Label. The annual cyberbullying survey. http://ditchthelabel.org/downloads/cyberbullying2013.pdf. [Online; accessed January-2016].
[2] S. Hinduja and J. W. Patchin, “Bullies Move Beyond the Schoolyard: A Preliminary Look at Cyberbullying,” Youth Violence And Juvenile Justice, vol. 4, 2006, pp. 148–169.
[3] H. Cowie. Cyberbullying and its impact on young people`s emotional health and well-being. The Psychiatrist, 37(5):167-170, 2013.
[4] D. Boyd, Why Youth (Heart) Social Network Sites: The Role of Networked Publics in Teenage Social Life. MacArthur Foundation Series on Digital Learning, Youth, Identity, and Digital Media, David Buckingham, Ed., MIT Press, Cambridge, MA, 2007
[5] Cybersafetysolutions.com. What can i do if i am cyber bullied. http://www.cybersafetysolutions.com.au-/factwhat- to-do-if-i-am-bullied.shtml. [Online; accessed January-2016]
[6] F. Mishra, M. Saini, and S. Solomon, Ongoing and online: Children and youth’s perceptions of cyber bullying. Children Youth Services Rev. 31, 12, 1222–1228, 2009
[7] K. Dinakar, R. Reichart, and H. Lieberman. Modeling the detection of textual cyberbullying. In The Social Mobile Web, 2011.
[8] F. Mishna, M. Saini, and S. Solomon, Ongoing and online: Children and youth’s perceptions of cyber bullying. Children Youth Services Rev. 31, 12, 1222–1228, 2009.
[9] K. Dinakar, R. Reichart, and H. Lieberman. Modeling the detection of textual cyberbullying. In The Social Mobile Web, 2011.
[10] Bharti, S. K., Babu, K. S., & Jena, S. K, “Parsing-based sarcasm sentiment recognition in twitter data,” 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining(ASONAM), Paris, 2015, pp. 1373-1380.
[11] M. Bouazizi and T. Otsuki Ohtsuki, "A Pattern-Based Approach for Sarcasm Detection on Twitter," in IEEE Access, vol. 4, pp. 5477- 5488, 2016.
[12] Rajadesingan, A., Zafarani, R., & Liu, H. (2015). “Sarcasm detection on twitter: A behavioral modelling approach.” 2015 WSDM - Proceedings of the 8th ACM International Conference on Web Search and Data Mining, pp. 97-106.
[13] Tungthamthiti, P., Shirai, K., & Mohd, M. (2014). “Recognition of sarcasm in tweets based on concept level sentiment analysis and supervised learning approaches.” 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014, pp. 404-413.
[14] Riloff, Ellen & Qadir, A & Surve, P & De Silva, L & Gilbert, N & Huang, R. “Sarcasm as contrast between a positive sentiment and negative situation.” Proceedings of EMNLP 2013, pp. 704-714.
[15] Clews P. & Kuzma J.(2017). “Rudimentary Lexicon Based Method for Sarcasm Detection.” International Journal of Academic Research and Reflection, 5(4), 24-33.
[16] N.Vijayalaksmi, Dr. A.Senthilrajan. “A hybrid approach for Sarcasm Detection of Social Media Data.” International Journal of 0Scientific and Research Publications (IJSRP), Volume 7, Issue 5, May 2017.