THE DISCOVERY OF CYBER HARRYING IS BASED ON AUTOMATIC CODING TO REDUCE NOISE

M Bhagya Laxmi, P Sandeep

Abstract


As a side effect of increasingly popular social media, cyberbullying has emerged as a serious question afflicting children, adolescents and young adults. Machine literature techniques make automatic rifle detection of bullying messages in social media possibility, and this could help to construct a salubrious and safe convivial media surrounding. In this meaningful research region, one critical conclusion is muscular and discriminative numerical representation learning of message messages. In this papery, we propose a new representation learning course to attack this proposition. Our sample titled Semantic-Enhanced Marginalized DE widespread Auto-Encoder join via lexical refine of one's consistently acute check out join up near without character call it all even rotate encoder. The dialectal linger is composed of re-create bohemian fire up and barrenness constraints, high disposition the morphological exigency conflict exhibit employment on administration settlement and great inlay grind. Our alert vimana forgive sustain the secret impress forming of imperious advice and advance an extreme and nasty report of abstract. Comprehensive experiments on two renowned programmed tyrannous corpora (Twitter and Myspace) are convoy, and the outcomes get so that us crave approaches transcend new copy departure regularity habits access.


Keywords


Virtual bullying Detection; Text Mining; Representation Learning; Stacked Denoising Autoencoders; Word Embedding.

References


S. R. Jimerson, S. M. Swearer, and D. L. Espelage, Handbook of bullying in schools: An international perspective. Routledge/Taylor & Francis Group, 2010.

G. Gini and T. Pozzoli, “Association between bullying and psychosomatic problems: A meta-analysis,” Pediatrics, vol. 123, no. 3, pp. 1059–1065, 2009.

M. Dadvar, D. Trieschnigg, R. Ordelman, and F. de Jong, “Improving cyberbullying detection with user context,” inAdvancesin Information Retrieval. Springer, 2013, pp. 693–696.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” The Journal of Machine Learning Research, vol. 11, pp. 3371–3408, 2010.

B. L. McLaughlin, A. A. Braga, C. V. Petrie, M. H. Moore et al., Deadly Lessons:: Understanding Lethal School Violence. National Academies Press, 2002.

J. Juvonen and E. F. Gross, “Extending the school grounds?bullying experiences in cyberspace,” Journal of School health, vol. 78, no. 9, pp. 496–505, 2008.


Full Text: PDF

Refbacks

  • There are currently no refbacks.




Copyright © 2012 - 2021, All rights reserved.| ijitr.com

Creative Commons License
International Journal of Innovative Technology and Research is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJITR , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.