A computational framework for detecting offensive language with support vector machine in social communities
Snehal B. Shende, Leena Deshpande · 2017
The use of the social media sites are growing rapidly to interact with the communities and to share the ideas among others. It may happen that most of the people dislike the ideas of others person views and make the use of the offensive language in their posts. Due to these offensive terms, many people especially youth and teenagers try to adopt such language and spread over the social media sites which may significantly affect the others people innocent minds. As offensive terms increasingly use by the people in highly manner, it is difficult to find or classify such offensive terms in real day to day life. To overcome from these problem, the proposed system analyze the offensive language and can classify the offensive sentence on a particular topic discussion using the support vector machine (SVM) as supervised classification in the data mining. The proposed system also can find the potential user by means of whom the offensive language spread among others and define the comparative analysis of SVM with Naive Bayes technique.