CYBER-BULLYING DETECTION USING MACHINE LEARNING AND NAÏVE BAYES AND N-GRAM MODEL

CYBER-BULLYING DETECTION USING MACHINE LEARNING AND NAÏVE BAYES AND N-GRAM MODEL
Author(s):
Mehul Goyal, Prof. Gauri Rao, Diksha Wali, Sarthak Yadav
Keywords:
Naïve Bayes Classification, N-Gram Model, Text Mining, Sentiment Analysis
Abstract
Many social media channels such as Facebook, Twitter, Instagram etc has altered our lives. People are mow connected to the world via these social media channels. These social media platforms have remarkable features but have their disadvantages too. Communicating through social media from a remote location and without revealing the real identity has given birth to a new crime that is CYBERBULLYING. Cyberbullying is basically misuse of this technology to tease, insult, harass or humiliate a person through internet. Many attempts have been introduced to prevent and decrease the number of cyberbullying cases however these methods rely on the interaction with the victim hence there is a need of method for cyberbullying detection where there is no involvement with the victim. In this paper we have reviewed and analysed existing models and propose a method for cyberbullying detection using Naïve Bayes Classification and N-Gram Model to scrutinize the bullying scenario or sentiment from each and every tweet collectively.
Article Details
Unique Paper ID: 152413 Publication Volume & Issue: Volume 8, Issue 3 Page(s): 648 - 651
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International conference on Management, Science, Technology, Engineering, Pharmact and Humanities.