Detecting Abusive Comments in Discussion Threads Using Naïve Bayes

Abstract

Comments are supported by various websites and provide a simple approach to increment user involvement. Users can generally comment on different types of media such as: social networks, blogs, forums and news articles. As discussions increasingly move toward online forums, the issue of insulting and abusive comments is becoming prevalent. In addition, a lots of comments are available due to these social media. Hence, it is not feasible for a human moderator to check each comments one by one and flag them as abusive or not abusive. For this reason, an automated classifier which is quick and efficient is necessary to detect such type of comments. To fulfill above purpose, in this paper a Naïve Bayes classifier is designed to detect abusive comments expressed in Bangla. Using a training corpus collected from “Youtube.com”, the Naïve Bayes classifier is employed to categorize comments as abusive or not abusive. Finally, the performance is evaluated by using 10-fold cross-validation on unprocessed data.