A Survey On Different Approaches of Detecting Cyber Bullying Messages
Keywords:
Social Media, Twitter,Machine Learning, Cyberbullying Detection.Abstract
Opinion mining or sentiment analysis is considered as an important application of NLP (Natural Language Processing). Opinion mining is extracting the views that people express online. Those Websites which permits social interaction and collaboration can be considered as social media site, including networking sites such as Facebook, MySpace, and Twitter. Such sites offer today's youth a platform for amusement, entertainment, thrill, correspondence and communication with friends and furthermore have developed radically and exponentially as of late. This is the reason, there are various side effects, as cyberbullying has emerged as a serious issue afflicting children, adolescents and young adults. Machine learning techniques have conceivable ability to make automatic detection of bullying messages in social media, and this could develop a healthy and comparatively safe social media environment. Social media getting more and more popular in our day today life. By the popularity of the social media affects the people who involving into it. This makes the technology to work or to feel smarter and makes us lazier. On resulting to this robust and discriminative numerical representation learning of text messages is a critical issue. Hence here we propose a learning method to tackle this issue which is named as Semantic Enhanced Marginalized Denoising Auto Encoder (smsda).
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