Cyberbullying Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder

ABSTRACT

Social Media isa group of Internetbasedapplications that build on the ideological andtechnological foundations of Web 2.0, and that allow thecreation and exchange of user-generated content.Via socialmedia, people can enjoy enormous information, convenientcommunication experience and so on. However, social mediamay have some side effects such as cyberbullying, whichmay have negative impacts on the life of people, especiallychildren and teenagers. Cyberbullying can be defined as aggressive, intentionalactions performed by an individual or a group of people viadigital communication methods such as sending messagesand posting comments against a victim. Different from traditionalbullying that usually occurs at school during face-to-face communication, cyberbullying on social media cantake place anywhere at any time.

EXISTING SYSTEM

As a side effect of increasingly popular social media, cyberbullying has emerged as a serious problem afflicting children,adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible,and this could help to construct a healthy and safe social media environment.

DIS ADVANTAGES

  • Cyberbullying has emerged as a serious problem afflicting children, adolescents and young adults.

PROPOSED SYSTEM

In this paper, we propose a new representation learningmethod to tackle this problem. Our method named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is developedvia semantic extension of the popular deep learning model stacked denoising autoencoder. The semantic extension consists ofsemantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and theword embedding technique. Our proposed method is able to exploit the hidden feature structure of bullying information and learn arobust and discriminative representation of text.

ADVANTAGES

  • Proposed method is able to exploit the hidden feature structure of bullying information
  • To learn a robust and discriminative representation of text.
  • It makes automatic detection of bullying messages in social media networks
  • And this could help to construct a healthy and safe social media environment.

MODULES

  • Marginalized Stacked Denoising Auto-encoder
  • Semantic Enhancement for mSDA
  • Construction of Bullying Feature Set
  • smSDA for Cyberbullying Detection

SYSTEM REQUIREMENTS

H/W System Configuration:-

Processor - Pentium –III

  • RAM - 256 MB (min)
  • Hard Disk - 20 GB
  • Key Board - Standard Windows Keyboard
  • Mouse - Two or Three Button Mouse
  • Monitor - SVGA

S/W System Configuration:-

  • Operating System : Windows95/98/2000/XP
  • Application Server : Tomcat5.0/6.X
  • Front End : HTML, Jsp
  • Scripts : JavaScript.
  • Server side Script : Java Server Pages.
  • Database : MySQL 5.0
  • Database Connectivity : JDBC

Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891

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