SENTIMENT/CYBER BULLYING DETECTION USING PYTHON & MACHINE LEARNING

Abstract

In the modern era, the usage of the internet has increased tremendously which in turn has led to the evolution of large amount of data. Cyber world has its own pros and cons. One of the alarming situations in web 4.0 is cyber bullying a type of cyber-crime. When bullying occurs online with the aid of technology it is known as cyber bullying. This research paper has surveyed the work done by 30 different researchers on cyber bullying, and elaborated on different methodologies adopted by them for the detection of bullying. Three types of features namely textual, behavioral and demographic features are extracted from the dataset as compared to earlier study over the same dataset where only textual features were considered. Textual features include certain bullying words that if exists within the text may lead to a true outcome for cyber bullying. Personality trait features are extracted for the user if it is involved once in bullying may bully in future too. While demographic features extracted from dataset include age, gender and location. The system is evaluated through different performance measures for both classifiers used the performance of the Support Vector Machine classifier is found better than the Bernoulli NB with overall 87.14 Accuracy. Keywords: Cyberbully, Support Vector Machine, Textual, classifier.