Cyber Bullying Detection using SGD Classifier
Dipali Himmatrao Patil, Gautami Kharul, Pranjali Gaikwad, Vaishali Khawse · Zenodo (CERN European Organization for Nuclear Research) · 2021
This paper describes a system for automatic detection of cyberbullying issues and challenges. The system is built to detect an activity on social media which can harm people such as hate speech, abusive language. People spread hatred toward a person in social networking. It does not affect only for health but also in many different aspects. Social media may have some side effects such as cyberbullying, which may have negative impacts on the life of people, especially children and teenagers. Automatic detection of such incidents requires intelligent systems. The subject discussed in this paper begins with a cyberbullying introduction: concept, categories and roles. Then, available data sources, features and classification methods used are analyzed in the discussion of cyberbullying identification. The famous methods used to classify bullying keywords inside the corpus are Natural Language Processing (NLP) and machine learning algorithms. The objective of this paper is to develop a social media network with some functionalities and demonstrate how cyberbullying can be prevented on social media.