DENOISING ENCODER WITH SEMANTICS AND EXCLUSION FOR SPOTTING CYBERBULLY
International Journal For Innovative Engineering and Management Research · 2020
The rapid growth of social networking is supplementing the progression of cyberbullying activities. Most of the individuals involved in these activities belong to the younger generations, especially teenagers, who are at more risk of suicidal attempts. Cyberbullying is the process of using the Internet, cell phones, or other devices to send or post text or images intended to hurt or embarrass another person. Through machine learning techniques, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyberbullying content. Here, we introduce a new machine learning method to deal with this problem. Our method named Semantic-Enhanced Marginalized Stacked Denoising Auto-Encoder (smSDA) is developed via a semantic extension of the popular deep learning model. The smSDA method detects the hidden attributes of the bullying information. Our approach experiments on two public cyberbullying corpora i.e. twitter and MySpace. The outcome of our proposed method is better than the other text representation learning methods.